Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Task-oriented Dialogue systems can be more suitable for human users. |
| Approach: | They propose a framework to optimize ToD systems by leveraging Multiple User SimulaTors. |
| Outcome: | The proposed framework improves performance on multiWOZ with human evaluations and automatic evaluations. |
SafeConv: Explaining and Correcting Conversational Unsafe Behavior (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets do not provide enough annotation to explain unsafe behavior . current chatbots generate toxic and offensive responses, which can be dangerous . |
| Approach: | They construct a dataset called SafeConv that provides comprehensive annotations for chatbots . they compare safe alternatives to rewrite unsafe responses . |
| Outcome: | The proposed model can explain unsafe behavior and detoxify chatbots, the authors show . the proposed model is able to detect unsafe utterances, extract unsafe spans, and convert unsafe responses to safe versions. |
Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that without artificially encouraging models to hallucinate, existing methods fall short . hallucinations are cases when the model generates output that is partially or fully unrelated to the source sentence. |
| Approach: | They propose a method that evaluates the percentage of the source contribution to a generated translation. |
| Outcome: | The proposed method improves detection accuracy for the most severe hallucinations by a factor of 2. |
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
| Approach: | They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models. |
Binary and Ternary Natural Language Generation (2023.acl-long)
Copied to clipboard
| Challenge: | ternary and binary neural networks have proven difficult to optimize since both parameter and output space are discretized . authors demonstrate ternaries and binary models on downstream tasks of summarization and machine translation . |
| Approach: | They propose to use ternary and binary neural networks to optimize for multiplication-free computation . they propose to apply statistics-based quantization for the weights and elastic quantization of the activations to the transformer text generation model. |
| Outcome: | The proposed model outperforms the best existing models on machine translation tasks. |
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing schema-guided dialogue state tracking models do not account for schema variations and are not generalized to unseen services. |
| Approach: | They propose a new architecture which allows for rich attention among descriptions and history while keeping computation costs constrained. |
| Outcome: | The proposed model outperforms the more than 30x larger D3ST-XXL model on the SGD-X benchmark by 5.0 points. |
EM Pre-training for Multi-party Dialogue Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to pretrain large language models for dialogue response generation are difficult due to the lack of annotated addressee labels in multi-party dialogue datasets. |
| Approach: | They propose an Expectation-Maximization approach that iteratively performs expectation steps to generate addressee labels and maximize a response generation model. |
| Outcome: | The proposed method is based on two-party dialogues and multi-party dialogs. |
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER (2023.acl-long)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a task of detecting linguistically complex named entities in low-context text. |
| Approach: | They propose a keyword-based augmentation approach to address the context-entity mismatch issue in complex name recognition (NER) they use selective masking to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entity. |
| Outcome: | The proposed approach outperforms baseline methods on monolingual, cross-lingual, and multilingual complex NER in various low-resource settings. |
Natural Language to Code Generation in Interactive Data Science Notebooks (2023.acl-long)
Copied to clipboard
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Oleksandr Polozov, Charles Sutton
| Challenge: | Data scientists use computational notebooks to perform data wrangling and analytic tasks. |
| Approach: | They build a benchmark program that synthesizes programs given NL intents from users by using a Python code language model. |
| Outcome: | The proposed model outperforms public code LMs in a dataset of 1078 code generation problems using the pandas data analysis framework in data science notebooks. |
Subset Retrieval Nearest Neighbor Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | k-nearest-neighbor machine translation (kNN-MT) is a new approach to improve NMT performance without additional training. |
| Approach: | They propose a method that integrates example-search into the decoding algorithm to improve neighbor token retrieval. |
| Outcome: | The proposed method achieves a speed-up of up to 132.2 times and an improvement in BLEU score of up 1.6 compared with kNN-MT in the WMT’19 translation task and the domain adaptation tasks in De-En and En-Ja. |
MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models generate toxic language which can cause security risks to their applications. |
| Approach: | They propose a method which detoxifies language models at token-level by interpolating it with a trained multiple instance learning network. |
| Outcome: | The proposed model outperforms baseline models in detoxification while hurting generation fluency a little bit. |
Dependency resolution at the syntax-semantics interface: psycholinguistic and computational insights on control dependencies (2023.acl-long)
Copied to clipboard
| Challenge: | Using psycholinguistic and computational experiments, we compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences. |
| Approach: | They compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences such as ‘José le prometió/ordenó a Mara ser ordenado/a’. |
| Outcome: | The models fail to identify the correct antecedent in non-adjacent dependencies, showing their reliance on linearity. |
Open-ended Long Text Generation via Masked Language Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained autoregressive language models have dominated OPen-ended Long Text Generation (Open-LTG) however, the low inference efficiency of AR impedes their usability. |
| Approach: | They propose a representative iterative non-autoregressive (NAR) decoding strategy to improve inference efficiency for Open-LTG. |
| Outcome: | The proposed model can generate short text and collapse for long text modeling. |
A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces (2023.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms for the linguistically oriented exploration of large neural language models include treating the model as a linguistic test subject by measuring output on test sentences and building probing classifiers on top of embeddings to test whether the embeddables are sensitive to certain properties like dependency structure. |
| Approach: | They project contextual embeddings into interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. |
| Outcome: | The proposed method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes. |
Holographic CCG Parsing (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for supertagging and parsing use black-box neural architectures to implicitly model phrase structure dependencies. |
| Approach: | They propose a method for formulating CCG as a recursive composition in a continuous vector space by using holographic embeddings as holography operator. |
| Outcome: | The proposed method can achieve comparable performance to state-of-the-art parsing with Transformers. |
Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on information extraction tasks focuses on one specific task, but in real-world scenarios, new data of different IE tasks and domains come in a stream over time. |
| Approach: | They propose a parameter- and deployment-efficient prompt tuning method to evaluate the UIE system under a “lifelong learning” setting. |
| Outcome: | The proposed method is able to learn new tasks without forgetting old ones and expand knowledge and functionalities without retraining the whole system. |
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
| Approach: | They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions . |
| Outcome: | The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation . |
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing factual consistency metrics are often uncontrollably generating text that is factually inconsistent with inputs. |
| Approach: | They propose a weakly supervised framework that is directly trained on actual generated samples from language models with weakly annotated labels. |
| Outcome: | The proposed framework improves on the TRUE benchmark by 3.3% over existing methods with 435M parameters. |
AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task. |
| Approach: | They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context. |
| Outcome: | The proposed model improves ABSA on four public datasets with 1.13% improvement over baselines. |
Text Adversarial Purification as Defense against Adversarial Attacks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for defending against adversarial examples are difficult due to the discrete nature of texts. |
| Approach: | They propose a novel adversarial purification method that aims to remove adversarials and make correct predictions based on the recovered clean samples. |
| Outcome: | The proposed method can defend against word-substitution adversarial attacks using language models. |
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres (2023.acl-long)
Copied to clipboard
| Challenge: | Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks. |
| Approach: | They propose to model complex dependency among event structured components with energy-based energy-modeling and represent event classes with simple but effective hyperspheres. |
| Outcome: | Experiments on two unified-annotated event datasets show that SPEECH is predominant in event detection and event-relation extraction tasks. |
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to content moderation are based on rule-based heuristics, but they lack the flexibility and robustness needed to moderate harmful content. |
| Approach: | They propose a novel contrastive learning approach for learning from logical rules for content moderation using only a few data examples. |
| Outcome: | The proposed approach outperforms state-of-the-art deep learning classifiers while providing more explainable predictions. |
What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns (2023.acl-long)
Copied to clipboard
| Challenge: | Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals. |
| Approach: | They compare 3rd-person pronoun translations to five other languages . they propose to address gender exclusivity in future research . |
| Outcome: | The proposed method compares translations of gendered vs. gender-neutral pronouns from english to five other languages and vice versa. |
What Is Overlap Knowledge in Event Argument Extraction? APE: A Cross-datasets Transfer Learning Model for EAE (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches ignore the overlap knowledge across datasets, preventing models from achieving better performance. |
| Approach: | They propose to divide the EAE knowledge into overlap knowledge across datasets and specific knowledge of the target dataset. |
| Outcome: | The proposed model outperforms the baseline model with a large margin when only ten records are available in the target dataset. |
Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time. |
| Approach: | They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute. |
| Outcome: | The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters. |
Knowledge of cultural moral norms in large language models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies do not examine moral variation in a diverse cultural setting. |
| Approach: | They investigate whether monolingual English language models capture moral variation across cultures . they use data from the World Values Survey and PEW global surveys . |
| Outcome: | The proposed models predict moral norms worse than the English models reported previously . the models improve inference across countries at the expense of an accurate estimate . |
Songs Across Borders: Singable and Controllable Neural Lyric Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Obtaining singable lyric translations can facilitate the globalization of the music publishing industry . |
| Approach: | They formalize lyric translation into a constrained translation problem and instantiate them to an English-Chinese system. |
| Outcome: | The proposed model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. |
Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)
Copied to clipboard
| Challenge: | Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue . |
| Approach: | They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison . |
| Outcome: | The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle. |
Revealing Single Frame Bias for Video-and-Language Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for video-and-language learning use multiple frames as inputs. |
| Approach: | They propose to use single-frame models for video-and-language learning to investigate temporality in video- and language tasks. |
| Outcome: | The proposed model does not take into account temporal information on video-and-language tasks. |
Learning with Partial Annotations for Event Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Event detection (ED) requires fully labeled and high-quality training data. |
| Approach: | They propose a new trigger localization formulation using contrastive learning to distinguish ground-truth triggers from contexts and show a decent robustness for addressing partial annotation noise. |
| Outcome: | The proposed approach achieves an F1 score of over 60% in an extreme scenario where 90% of events are unlabeled. |
World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | GOVA examines grounding and bootstrapping in open-world language learning. |
| Approach: | They propose a visually-grounded language model that uses grounding as an objective . they propose GOVA to investigate grounding and bootstrapping in open-world language learning . |
| Outcome: | The proposed model is faster and faster grounded than previous models, the authors show . they show that grounding helps the model to learn unseen words more rapidly and robustly . |
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that language models can rely on shallow patterns in problem description when generating a solution. |
| Approach: | They propose a framework which pins down the causal effect of various factors on the output solution. |
| Outcome: | The proposed framework improves robustness and sensitivity to direct interventions on a test bed of math word problems. |
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods for open-domain dialogues are difficult due to the one-to-many issue of the open- domain dialogues. |
| Approach: | They propose a learning-based automatic evaluation metric which can robustly evaluate open-domain dialogues by augmenting CVAEs with a Next Sentence Prediction objective and employing Mutual Information to model the semantic similarity of text in the latent space. |
| Outcome: | The proposed method can evaluate open-domain dialogues on two open- domain dialogue datasets. |
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) can be used to generate text data for training and evaluating other models. |
| Approach: | They propose to use logit suppression and temperature sampling to diversify text generation but at the cost of data accuracy. |
| Outcome: | The proposed approach can increase diversity but at the cost of data accuracy. |
Pruning Pre-trained Language Models Without Fine-Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to prune Pre-trained Language Models (PLMs) are overparameterized and require fine-tuning. |
| Approach: | They propose a pruning method that uses first-order pruning to prune PLMs while fine-tuning the remaining weights. |
| Outcome: | The proposed method outperforms first-order pruning and zero-order methods at sparsity levels. |
When Does Translation Require Context? A Data-driven, Multilingual Exploration (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. |
| Approach: | They develop a multilingual discourse-aware benchmark to evaluate model performance on discourse phenomena in a given dataset. |
| Outcome: | The proposed model improves on previously studied phenomena while uncovering others which were not addressed. |
Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for multi-modal fake news detection neglect the fact that some label-specific features cannot generalize well to the testing set, thus suffering from the latent data bias. |
| Approach: | They propose a Causal intervention and Counterfactual reasoning based debiasing framework for multi-modal fake news detection that eliminates the image-only bias by deducting the direct effect of the image from the total effect on labels. |
| Outcome: | The proposed framework eliminates the psycholinguistic bias in the text and the bias of inferring news label based on only image features. |
LexSym: Compositionality as Lexical Symmetry (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to generalize compositional models fail to generalise from small datasets. |
| Approach: | They propose a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. |
| Outcome: | The proposed procedure matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and Alchemy instruction following, and CLEVR-CoGenT visual question answering datasets. |
Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition (2023.acl-long)
Copied to clipboard
Jun Sun, Shoukang Han, Yu-Ping Ruan, Xiaoning Zhang, Shu-Kai Zheng, Yulong Liu, Yuxin Huang, Taihao Li
| Challenge: | Existing studies focus on developing models that exploit the unification of multiple modalities. |
| Approach: | They propose to maintain modality independence by using a multi-modal transformer model that fuses all modalities. |
| Outcome: | The proposed model outperforms state-of-the-art models in multi-modal emotion recognition. |
CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection. |
| Approach: | They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information. |
| Outcome: | The proposed method improves on three text classification tasks on four advanced attack algorithms. |
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest (2023.acl-long)
Copied to clipboard
Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi
| Challenge: | Large neural networks can generate jokes, but do they really “understand” humor? a new challenge challenges AI models to match a joke to a cartoon, identify a winning caption, and explain why a winner is funny. |
| Approach: | They propose three tasks based on the New Yorker Cartoon Caption Contest . they aim to match a joke to a cartoon, identify a winning caption and explain why it's funny . |
| Outcome: | The proposed tasks are based on the New Yorker Cartoon Caption Contest . they include matching a joke to a cartoon, identifying a winning caption, and explaining why a funny caption is funny. |
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Using self-training or text-to-speech (TTS) to improve low-resource ASR performance is costly and can lead to catastrophic forgetting. |
| Approach: | They examine whether data augmentation techniques could help improve low-resource ASR performance . they use self-training to generate transcriptions, which are combined with original data to train new system . |
| Outcome: | The proposed approach yields a 20.5% reduction in WER compared to a system trained on 24 minutes of manually transcribed speech. |
CLCL: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Non-compositional expressions are a substantial challenge for natural language processing systems, necessitating more intricate processing compared to general language tasks. |
| Approach: | They propose a dynamic curriculum learning framework specifically designed to take advantage of scarce available training data for modeling non-compositionality. |
| Outcome: | The proposed framework improves on idiom usage recognition and metaphor detection tasks. |
Multi-VALUE: A Framework for Cross-Dialectal English NLP (2023.acl-long)
Copied to clipboard
| Challenge: | Current systems that focus on standard American English are not dialect invariant . current systems focus on a single dialect, which results in performance discrepancies . |
| Approach: | They propose a resource for evaluating and achieving English dialect invariance . they stress test question answering, machine translation, and semantic parsing . |
| Outcome: | The proposed system is based on a rule-based translation system spanning 50 English dialects and 189 unique linguistic features. |
Self-Edit: Fault-Aware Code Editor for Code Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Large language models (LLMs) have low pass rates and accuracy on competitive programming tasks. |
| Approach: | They propose a generate-and-edit approach that uses execution results of generated code from LLMs to improve code quality on competitive programming tasks. |
| Outcome: | The proposed method improves pass@1 by 89% on APPS-dev, 31% on apps-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. |
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning (2023.acl-long)
Copied to clipboard
| Challenge: | Pretraining has been shown to scale well with compute, data size and data diversity. |
| Approach: | They propose a method that provides benefits of multitask learning but leverages distributed computation . they propose 'coldfusion' can create synergistic loop where finetuned models can be "recycled" |
| Outcome: | The proposed method outperforms RoBERTa and previous multitask models on 35 datasets. |
Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization (2023.acl-long)
Copied to clipboard
| Challenge: | evaluators of machine translation systems often use text-based metrics to evaluate performance . however, these metrics lack semantic-level information and exhibit poor correlation with human ratings . authors propose a method to reduce inference bias of neural metrics in out-of-distribution data . |
| Approach: | They propose to reduce inference bias by using uncertainty estimation, test-time adaptation, and inference to reduce model uncertainty. |
| Outcome: | The proposed method reduces model uncertainty and improves correlation performance across models. |
Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling (2023.acl-long)
Copied to clipboard
| Challenge: | ensembling BERT models often improves accuracy but at the cost of significantly more computation and memory footprint. |
| Approach: | They propose a new ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. |
| Outcome: | The proposed method outperforms existing BERT models on GLUE and SuperGLUE with 100 training samples. |
On-the-fly Cross-lingual Masking for Multilingual Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | In multilingual pre-training, multilingual models only learn cross-linguality implicitly from isomorphic spaces formed by overlapping different language spaces due to the lack of explicit cross-linguistic forward pass. |
| Approach: | They propose a dynamic token-wise masking scheme for multilingual pre-training that uses a special token [C]x to replace a random token in the input sentence. |
| Outcome: | The proposed model improves the performance of UNMT models on De, Ro, Ne En. |
How About Kind of Generating Hedges using End-to-End Neural Models? (2023.acl-long)
Copied to clipboard
| Challenge: | hedging is a strategy for softening the impact of a statement in conversation. |
| Approach: | They propose to fine-tune state-of-the-art language models trained on human-human tutoring data and then use a hedge classifier to select the candidate that best matches the expected hedging strategy. |
| Outcome: | The proposed model is feasible in a noisy environment with reranking, and it is compared with other approaches. |
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in diffusion models have enabled high-quality image generation . generating images with desired details requires proper prompts . |
| Approach: | They analyze syntactic and semantic characteristics of diffusion models and their prompts . they pinpoint specific hyperparameter values and prompt styles that can lead to model errors . |
| Outcome: | The first large-scale text-to-image prompt dataset totals 6.5TB . it contains 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. |
From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Key Point Analysis (KPA) is a new method for analyzing textual comments . it uses a list of concise sentences or phrases to extract key points from data . |
| Approach: | They propose to organize key points into a hierarchy according to their specificity . they compare methods for predicting pairwise relations between key points . |
| Outcome: | The proposed method improves on predicting pairwise key point relations and weak supervision. |
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications (2023.acl-long)
Copied to clipboard
| Challenge: | Existing consensus on which OpenIE model is best for each application is lacking . different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate OpenIE system for one’s applications. |
| Approach: | They propose to use OpenIE to extract relation tuples from plain text to compare different models and training sets to find the best model for their applications. |
| Outcome: | The proposed models perform well on a Complex QA application. |
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Annotator disagreements are resolved before learning takes place, but researchers question the performance of a system when annotators disagree. |
| Approach: | They propose a method that uses language features and label distributions to pool similar items into larger labels. |
| Outcome: | The proposed method is based on five publicly available datasets with varying levels of disagreements on social media and in the wild using a dataset from Facebook. |
Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA (2023.acl-long)
Copied to clipboard
| Challenge: | Despite remarkable progress made in natural language processing, even the state-of-the-art systems often make incorrect predictions. |
| Approach: | They propose to use selective prediction to enable models to abstain from answering when their predictions are likely to be incorrect. |
| Outcome: | The proposed method improves performance on 11 QA datasets and in- and out-of-domain settings. |
UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs . |
| Approach: | They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music. |
| Outcome: | The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation. |
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression. |
| Approach: | They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression. |
| Outcome: | The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline. |
Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to detect whether natural language sequences are metaphoric or literal focus on detecting the transfer of knowledge structures to pre-trained language models. |
| Approach: | They propose to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor’s source domain without any pre-set domains. |
| Outcome: | The proposed model generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish. |
Being Right for Whose Right Reasons? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work has failed to acknowledge that what counts as a rationale is subjective. |
| Approach: | They propose to use demographic annotations to augment existing datasets to ask what demographics our models align with and whose reasoning patterns they align with. |
| Outcome: | The proposed model rationales align better with older and/or white annotators, and are biased towards older and white anorators. |
ALERT: Adapt Language Models to Reasoning Tasks (2023.acl-long)
Copied to clipboard
Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissi, Siddharth Verma, Zhijing Jin, Gargi Ghosh, Mona Diab, Asli Celikyilmaz
| Challenge: | Large language models have shown increasing in-context learning capabilities with scaling up the model and data sizes. |
| Approach: | They propose a benchmark and suite of analyses to evaluate reasoning skills of large language models. |
| Outcome: | The proposed model compares pre-trained and fine-tuned models on tasks that require reasoning skills to solve. |
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages (2023.acl-long)
Copied to clipboard
Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze
| Challenge: | Lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world. |
| Approach: | They create a model that scales LLMs horizontally and a corpus that covers 511 low-resource languages. |
| Outcome: | The proposed model improves on five diverse tasks across low- and high-resource languages. |
Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses. |
| Approach: | They propose a constrained learning framework with boundary-adjusting for Emotion-Cause Pair Extraction that summarizes prior rules and forces the model to take them into consideration in optimization. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods on unbalanced data and proves robustness on unbalancing data. |
Pretrained Bidirectional Distillation for Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on language knowledge transfer from pretrained models to neural machine translation models. |
| Approach: | They propose to use masked language pretraining to efficiently transfer bidirectional language knowledge to NMT models. |
| Outcome: | The proposed method can significantly improve machine translation performance and achieve competitive or even better results than previous methods. |
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)
Copied to clipboard
Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyungmin Kim, Jung-Woo Ha, Sang-Woo Lee
| Challenge: | Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. |
| Approach: | They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality. |
| Outcome: | The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services. |
Improving Continual Relation Extraction by Distinguishing Analogous Semantics (2023.acl-long)
Copied to clipboard
| Challenge: | Existing works store a small number of typical samples to re-train the model for alleviating forgetting. |
| Approach: | They propose a continual relation extraction model that uses memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem. |
| Outcome: | The proposed model outperforms existing models on analogous relations and overcomes overfitting problem. |
Improving Pretraining Techniques for Code-Switched NLP (2023.acl-long)
Copied to clipboard
| Challenge: | Multilingual pretraining models for code-switched inputs are a key component of NLP applications. |
| Approach: | They propose to use masked language modeling techniques to mask code-switched text that are cognizant of language boundaries prior to masking. |
| Outcome: | The proposed techniques improve performance on two downstream tasks, Question Answering (QA) and Sentiment Analysis (SA), compared to standard pretraining techniques. |
A Theory of Unsupervised Speech Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Unsupervised speech recognition (ASR) is the problem of learning automatic speech recognition systems from unpaired speech-only and text-only corpora. |
| Approach: | They propose a general theoretical framework to study the properties of pasted macro ‘ASRU’/ systems based on random matrix theory and the theory of neural tangent kernels. |
| Outcome: | The proposed framework proves various learnability conditions and sample complexity bounds on synthetic languages with three classes of transition graphs. |
ThinkSum: Probabilistic reasoning over sets using large language models (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have a substantial capacity for high-level analogical reasoning, but they fail in scenarios that require reasoning over multiple objects or facts and making sequences of logical deductions. |
| Approach: | They propose a two-stage probabilistic inference paradigm, ThinkSum, which reasons over sets of objects or facts in a structured manner. |
| Outcome: | The proposed paradigm improves on the BIG-bench suite of evaluation tasks. |
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist (2023.acl-long)
Copied to clipboard
| Challenge: | a systematic review of automatic evaluation metrics for Natural Language Generation (NLG) shows that task-agnostic metrics have a weak correlation with human . |
| Approach: | They propose a framework to assess the effectiveness of automatic metrics in three NLG tasks . they propose task-agnostic and human-aligned metrics to be used for evaluation . |
| Outcome: | The proposed framework provides access to the evaluation tools for three NLG tasks. |
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | a context leads to various responses, and a response answers multiple contexts. |
| Approach: | They propose a method that augments open-domain dialogue generation from a many-to-many perspective. |
| Outcome: | The proposed method can augment open-domain dialogue generation tasks with automatic and human evaluation. |
TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge. |
| Approach: | They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts. |
Consistency Regularization Training for Compositional Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing neural models have difficulty generalizing to unseen combinations of seen components. |
| Approach: | They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures. |
| Outcome: | The proposed model performs well on semantic parsing and machine translation benchmarks. |
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)
Copied to clipboard
Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan
| Challenge: | Existing work generates long videos segment by segment sequentially, which is inefficient. |
| Approach: | They propose a Diffusion over Difference architecture for eXtremely Long video generation. |
| Outcome: | The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence. |
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe (2023.acl-long)
Copied to clipboard
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
| Challenge: | Privacy concerns have increased in data-driven products due to the tendency of machine learning models to memorize sensitive training data. |
| Approach: | They propose a method for generating useful synthetic text with a formal privacy guarantee by fine-tuning a pretrained generative language model with DP. |
| Outcome: | The proposed method produces synthetic text competitive in terms of utility with its non-private counterpart, while providing strong protection against potential privacy leakages. |
A Close Look into the Calibration of Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. |
| Approach: | They conduct fine-grained control experiments to study the dynamic change in PLMs’ calibration performance in training. |
| Outcome: | The proposed methods significantly reduce PLMs’ confidence in wrong predictions. |
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data. |
| Approach: | They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain. |
| Outcome: | The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings. |
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction (2023.acl-long)
Copied to clipboard
| Challenge: | Natural language video localization (NLVL) aims to localize a temporal moment from an untrimmed video that semantically corresponds to a given text query. |
| Approach: | They propose a proposal-based solution that generates proposals and selects the best matching proposal. |
| Outcome: | The proposed solution is faster than existing approaches on three public datasets. |
Diverse Demonstrations Improve In-context Compositional Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning has shown great success in i.i.d semantic parsing splits . however, in compositional generalization, selecting similar demonstrations is insufficient . |
| Approach: | They propose a method to select diverse demonstrations that collectively cover all the structures required in the output program and encourage the model to generalize to new structures from these demonstrations. |
| Outcome: | The proposed method improves performance across three compositional generalization datasets and finetuning. |
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning is a common practice to randomly sample examples to serve as context. |
| Approach: | They propose a new principle for in-context learning that helps each sample find an in-constitut example organization that can derive the correct prediction. |
| Outcome: | The proposed method achieves 40% relative improvement over the common practice setting. |
On the Efficacy of Sampling Adapters (2023.acl-long)
Copied to clipboard
| Challenge: | Using sampling adapters can improve the quality of the generated text. |
| Approach: | They propose a framework for understanding sampling adapters and propose 'sampling adapters' they argue that the shift enforced by them can be viewed as a trade-off between precision and recall . |
| Outcome: | The proposed framework can be used to improve the quality of language models by modifying their distributions to improve their precision and recall. |
Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain. |
| Approach: | They propose a domain-adaptive language model to generate labeled data from a source domain. |
| Outcome: | The proposed approach outperforms existing methods on ABSA and Aspect Extraction tasks. |
Compositional Data Augmentation for Abstractive Conversation Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Abstractive conversation summarization systems rely on large-scale annotated summaries, but collecting and annotating these conversations can be time-consuming and labor-intensive. |
| Approach: | They propose a method for generating diverse and high-quality pairs of conversations and summaries by extracting conversation structures and organizing meaningful conversation snippets. |
| Outcome: | The proposed method outperforms baseline methods on SAMSum and DialogSum datasets and achieves a 10% increase in ROUGE scores with limited data. |
PMAES: Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring (2023.acl-long)
Copied to clipboard
| Challenge: | Current cross-prompt automated essay scoring systems are limited by their ability to extract features directly from the original prompt. |
| Approach: | They propose a method to learn more shared features between the source and target prompts by using a "prompt-mapping" approach to obtain more shared feature representations between the two prompts . |
| Outcome: | The proposed method can be applied to a ASAP++ dataset showing that it is highly efficient and consistent. |
Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to measure stereotypes in large language models rely on manual templates or natural sentences that contain stereotypes. |
| Approach: | They propose a prompt-based method to measure stereotypes in large language models . they use natural language descriptions of the target demographic group alongside unmarked defaults . |
| Outcome: | The proposed method detects that portrayals contain higher rates of racial stereotypes than human-written portrayals. |
On Prefix-tuning for Lightweight Out-of-distribution Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Out-of-distribution (OOD) detection is a fundamental task vexing real-world applications . fine-tuning based methods require storing fine- tuned models for each scenario . |
| Approach: | They propose an unsupervised prefix-tuning based OOD detection framework called PTO . they propose to take advantage of optional training data labels and targeted OOD data . |
| Outcome: | The proposed framework performs better than existing methods under a wide range of metrics, detection settings, and OOD types. |
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding (2023.acl-long)
Copied to clipboard
| Challenge: | grammatical error correction is an important NLP task that is usually solved with autoregressive sequence-to-sequence models. |
| Approach: | They propose a non-autoregressive approach to grammatical error correction that decouples a permutation network and a decoder network that fills in specific tokens. |
| Outcome: | The proposed approach improves over previously known non-autoregressive methods and reaches the level of autoregressive approaches that do not use language-specific synthetic data generation methods. |
Measuring Progress in Fine-grained Vision-and-Language Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | X-VLM models lack "fine-grained" understanding of relationships, verbs and numbers in images . pretraining on large-scale image–text data from the Web has facilitated rapid progress on many vision-and-language tasks . |
| Approach: | They investigate models that outperform other baselines on fine-grained data . they highlight importance of novel losses and rich data sources for learning fine-grain skills . |
| Outcome: | The proposed model outperforms baseline models on four fine-grained benchmarks . the model outpersforms other baseline models and even degrades performance . |
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information (2023.acl-long)
Copied to clipboard
| Challenge: | Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. |
| Approach: | They propose an unsupervised approach that incorporates sense definitions when sense information of the answer is not provided. |
| Outcome: | The proposed approach improves the performance of the existing definition generation method in OOD examples. |
Chain-of-Skills: A Configurable Model for Open-Domain Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Using customized retrieval models, model transferability and scalability are limited. |
| Approach: | They propose a modular retrieval model where individual modules correspond to key skills that can be reused across datasets. |
| Outcome: | The proposed model outperforms self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. |
Elaboration-Generating Commonsense Question Answering at Scale (2023.acl-long)
Copied to clipboard
| Challenge: | elaborations are generated using language models that generate background knowledge that helps improve performance . human evaluations show that the quality of the generated ellaborations is high . |
| Approach: | They propose to finetune smaller language models to generate useful intermediate context . they compare a language model with an answer predictor and generate elaborations . human evaluations show that the quality of the generated ellaborations is high . |
| Outcome: | The proposed framework outperforms other models on commonsense questions on four commons sense benchmarks. |
Neural Unsupervised Reconstruction of Protolanguage Word Forms (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for reconstructing ancient word forms use expectation-maximization . past work has used this method to predict simple phonological changes . |
| Approach: | They extend expectation-maximization to predict phonological changes between ancient word forms and their cognates in modern languages. |
| Outcome: | The proposed model reduces edit distance from the target word forms compared to previous methods. |
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model. |
| Approach: | They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set. |
| Outcome: | The proposed framework reduces label noise and preserves hard examples while maintaining accuracy. |
On Evaluating Multilingual Compositional Generalization with Translated Datasets (2023.acl-long)
Copied to clipboard
| Challenge: | a growing amount of research investigating compositional generalization in NLP is done on English . a critical semantic distortion is a limitation of the translation of datasets . |
| Approach: | They propose to translate a dataset for evaluating compositional generalization in semantic parsing. |
| Outcome: | The proposed benchmarks show that the translation of the MCWQ dataset suffers from semantic distortion. |
FAA: Fine-grained Attention Alignment for Cascade Document Ranking (2023.acl-long)
Copied to clipboard
| Challenge: | Contemporary document ranking methods focus on transforming documents into passages to handle long inputs, but intensive query-irrelevant content may lead to harmful distraction and high query latency. |
| Approach: | They propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model. |
| Outcome: | Experiments on MS MARCO and TREC DL show that the proposed method is effective in document ranking tasks. |
Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models are overly parameterized and have significant redundancy . recent studies show that PLMs are highly over-parameterized and robust to pruning . |
| Approach: | They propose to re-parameter and fine-tune pre-trained language models from a new perspective: Discovery of intrinsic task-specific subspace. |
| Outcome: | The proposed model can be fine-tuned in the subspace with a small number of free parameters. |
Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to provide emotional support (ESC) ignore the effect on ES and lack explicit goals to guide emotional positive transition. |
| Approach: | They propose a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation. |
| Outcome: | The proposed model outperforms existing models in achieving positive emotion elicitation while maintaining conversational goals like coherence. |
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to retrieve knowledge-intensive conversations are based on external resources such as Wikipedia databases or search engine results. |
| Approach: | They propose an unsupervised query enhanced approach for knowledge-intensive conversations . they conduct experiments on three knowledge- intensive conversation datasets . |
| Outcome: | The proposed approach performs better than all unsupervised methods across three datasets and achieves competitive performance compared to supervised methods. |
Why Aren’t We NER Yet? Artifacts of ASR Errors in Named Entity Recognition in Spontaneous Speech Transcripts (2023.acl-long)
Copied to clipboard
Piotr Szymański, Lukasz Augustyniak, Mikolaj Morzy, Adrian Szymczak, Krzysztof Surdyk, Piotr Żelasko
| Challenge: | despite advances in language models, the transcript of spontaneous human-human conversations remains an insurmountable challenge for most models. |
| Approach: | They examine the relationship between ASR and NER errors which limit NER models' ability to recover entity mentions from spontaneous speech transcripts. |
| Outcome: | The proposed model fails even if no word errors are introduced by the ASR . the proposed model's performance deteriorates when applied to the ASL outputs . |
Precise Zero-Shot Dense Retrieval without Relevance Labels (2023.acl-long)
Copied to clipboard
| Challenge: | Existing dense retrieval systems that use semantic embedding similarities can be effective across tasks and languages. |
| Approach: | They propose to pivot through Hypothetical Document Embeddings (HyDE) given a query, HyDE first zero-shot prompts an instruction-following language model to generate a hypothetical document. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across tasks and languages. |
White-Box Multi-Objective Adversarial Attack on Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained transformers are popular in state-of-the-art dialogue generation systems . however, they are vulnerable to adversarial samples crafted by small and imperceptible perturbations. |
| Approach: | They propose a multi-objective attack method that balances two objectives: generation accuracy and length. |
| Outcome: | The proposed method significantly degrades state-of-the-art DG models with a higher success rate than traditional accuracy-based methods. |
A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules (2023.acl-long)
Copied to clipboard
| Challenge: | Explicit linguistic knowledge encoded by rule-based morphological analyzers is expensive and non-trivial . creating such resources is tedious and requires additional efforts to extract human-interpretable patterns from them. |
| Approach: | They propose a method for automatically learning morphophonological rules of Arabic from a corpus. |
| Outcome: | The proposed approach produces a set of generalizable rules from a dataset. |
Few-shot Adaptation Works with UnpredicTable Data (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. |
| Approach: | They finetuned 413,299 tasks from internet tables to find narrow subsets outperform more diverse datasets. |
| Outcome: | The proposed model outperforms training on 40 human-curated NLP datasets on 52 downstream tasks, but not proportionally to dataset scale. |
Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers (2023.acl-long)
Copied to clipboard
| Challenge: | CSJ is a task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work. |
| Approach: | They propose to combine CSJ tasks SELECT, SIMPLIFY and REWRITE to produce cross-lingual simplified science summaries for non-expert readers. |
| Outcome: | The proposed task outperforms existing solutions on Wikipedia and can serve as a strong baseline for future work. |
HuCurl: Human-induced Curriculum Discovery (2023.acl-long)
Copied to clipboard
| Challenge: | Existing curriculum learning frameworks can be used to discover effective curricula for NLP tasks based on prior knowledge about sample difficulty. |
| Approach: | They propose a framework for curriculum learning based on prior knowledge about sample difficulty. |
| Outcome: | The proposed framework outperforms existing curriculum learning approaches on several NLP tasks and can prune and weight samples for better learning. |
kNN-TL: k-Nearest-Neighbor Transfer Learning for Low-Resource Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Transfer learning is an effective technique for enhancing low-resource neural machine translation (NMT) however, these methods do not make use of the parent knowledge during the child inference, which may limit the translation performance. |
| Approach: | They propose a k-Nearest-Neighbor Transfer Learning approach which leverages the parent knowledge throughout the entire developing process of the child model. |
| Outcome: | The proposed approach outperforms strong baselines on four low-resource translation tasks. |
Do language models have coherent mental models of everyday things? (2023.acl-long)
Copied to clipboard
| Challenge: | Psychologists and cognitive scientists hypothesize that humans develop mental models of the world, namely internal, conceptual representations of the environment which we base our decisions and actions on. |
| Approach: | They propose to add a constraint satisfaction layer to the LM's raw predictions to apply commonsense constraints to reduce incoherence. |
| Outcome: | The proposed extension removes inconsistencies and improves accuracy by 16-20%. |
Rogue Scores (2023.acl-long)
Copied to clipboard
| Challenge: | Critical evaluation decisions and parameters are routinely omitted, making most reports irreproducible . Thousands of papers use nonstandard evaluation packages with software defects that produce incorrect scores. |
| Approach: | a systematic review of over two thousand papers using a popular metric called ROUGE finds errors . critical evaluation decisions and parameters are routinely omitted, making most reported scores irreproducible . a large number of ROUGEE model evaluation scores have been incorrectly computed . |
| Outcome: | a systematic review of over two thousand papers finds that ROUGE scores are incorrect . the metric is widely used in machine learning and is inconsistent with human evaluations . |
Instruction Induction: From Few Examples to Natural Language Task Descriptions (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models can perform unseen tasks by conditioning on a few input-output demonstrations, but task inference is implicit and the ability of models to explicitly reason about it remains unexplored. |
| Approach: | They propose an instruction induction challenge in which a model is asked to generate a natural language instruction that fits a set of labeled examples. |
| Outcome: | The proposed model achieves 65.7% of human performance while the original model only reaches 9.8% of human performances. |
In-Context Analogical Reasoning with Pre-Trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. |
| Approach: | They apply large pre-trained language models to visual Raven’s Progressive Matrices (RPM) and use language-based abstractions to support analogy in AI systems. |
| Outcome: | The proposed language-based abstractions outperform human models on Raven’s Progressive Matrices and supervised vision-based methods. |
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Among recent NLP research, multi-document processing is gaining increasing attention due to the need to handle and process an increasing amount of textual data and available documents online. |
| Approach: | They propose to pre-train a generic multi-document model from a cross-document question answering pre-training objective by generating salient sentences from one document and challenging it to recover the sentence from which it was generated. |
| Outcome: | The proposed model outperforms zero-shot GPT-3.5 and GPT-4 in multiple document tasks and generates the correct answer and the salient sentence from a salient document. |
Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent success of natural language processing (NLP) is driven by the adoption of large-scale pretrained language models. |
| Approach: | They propose a method to determine the impact of distillation influence on student generalization ability by prioritizing samples likely to enhance the student's generalization abilities. |
| Outcome: | The proposed method outperforms 10 common knowledge distillation baselines on 6 text classification tasks in the GLUE benchmark. |
REV: Information-Theoretic Evaluation of Free-Text Rationales (2023.acl-long)
Copied to clipboard
| Challenge: | Existing metrics for rationale evaluation focus on the association between the rationale and a label, whereas REV is more sensitive to new information in free-text rationales. |
| Approach: | They propose a metric called REV to quantify the amount of new, label-relevant information in a rationale beyond the information already available in the input or the label. |
| Outcome: | The proposed metric is consistent with human judgments on rationale evaluations and provides more sensitive measurements of new information in free-text rationales. |
ELQA: A Corpus of Metalinguistic Questions and Answers about English (2023.acl-long)
Copied to clipboard
| Challenge: | ELQA corpus is metalinguistic—it consists of language about language. |
| Approach: | They present a corpus of questions and answers in and about the English language . they use a free-form question answering task and multiple LLMs to analyze their capacity . |
| Outcome: | The ELQA corpus covers grammar, meaning, fluency, and etymology . the results can be used to investigate metalinguistic capabilities of NLU models . |
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system. |
| Approach: | They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism. |
| Outcome: | The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism. |
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba (2023.acl-long)
Copied to clipboard
| Challenge: | Bemba is the most populous language of Zambia but lacks resources for research . despite its significance, Bemba remains under-resourced and lacking in high-quality data and resources for NLP experiments and language technologies. |
| Approach: | They propose a large multimodal dataset for Bemba that includes images, transcriptions and translations. |
| Outcome: | The proposed dataset is based on images, transcriptions and translations of Bemba speakers . it provides baselines on speech recognition, machine translation and speech translation tasks . |
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on USM neglect explicitly modeling the user’s task goals fulfillment using the task schema. |
| Approach: | They propose a schema-guided user satisfaction modeling framework that explicitly models the degree to which the user’s preferences regarding task attributes are fulfilled by the system. |
| Outcome: | The proposed framework outperforms existing methods on benchmark datasets and shows that it can interpret and scale well with unseen tasks and can work in low-resource settings. |
Robust Multi-bit Natural Language Watermarking through Invariant Features (2023.acl-long)
Copied to clipboard
| Challenge: | Recent years have witnessed a proliferation of valuable original natural language contents found in subscription-based media outlets, web novel platforms, and outputs of large language models. |
| Approach: | They propose a multi-bit watermarking framework that embeds information and extracts watermarks in a robust manner despite possible corruption by following a well-known proposition from image watermark. |
| Outcome: | The proposed model improves on the previous work on payload and robustness by +16.8% point on average on four datasets, three corruption types, and two corruption ratios. |
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to infuse knowledge graphs with pre-trained LMs are limited by the input sequence length. |
| Approach: | They propose a language model that leverages knowledge in local, document-level, and global contexts for long document understanding. |
| Outcome: | The proposed model achieves state-of-the-art on three long document understanding tasks across 6 datasets/settings. |
AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Attribute extraction aims to identify attribute names and the corresponding attribute values from descriptive texts. |
| Approach: | They propose a unified formulation for real-world attribute extraction application, where closed-world, open-world and semi-open attribute extraction tasks are modeled uniformly. |
| Outcome: | The proposed model outperforms existing methods on three datasets and outperformed existing methods by a large margin. |
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Abstractive summarization is less prone to unfaithfulness issues than abstractive summaries . but, unfaitfulness problems, i.e., hallucinating new information, are still a problem in extractive summarisation . |
| Approach: | They propose a typology with five types of broad unfaithfulness problems that can appear in extractive summaries, including and beyond not-entailment. |
| Outcome: | The proposed metric shows that it detects unfaithful summaries faster than existing faithfulness evaluation metrics. |
Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)
Copied to clipboard
| Challenge: | State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment. |
| Approach: | They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs. |
| Outcome: | The proposed method improves the estimation performance while mitigating the bias. |
Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation (2023.acl-long)
Copied to clipboard
| Challenge: | a genetic algorithm (GA) based method improves MT quality and identifies weaknesses in evaluation metrics. |
| Approach: | They propose a genetic algorithm-based method for modifying n-best lists produced by a machine translation system using a fitness function. |
| Outcome: | The proposed method improves translation quality and identifies weaknesses in evaluation metrics. |
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)
Copied to clipboard
| Challenge: | A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections. |
| Approach: | They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system. |
| Outcome: | The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values . |
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion (2023.acl-long)
Copied to clipboard
| Challenge: | Prior denoising methods suppress redundant and noisy information at risk of losing critical information. |
| Approach: | They propose a denoising bottleneck fusion model for fine-grained video multimodal fusion . they employ a bottleneck mechanism to filter out noise and redundancy with a restrained receptive field . |
| Outcome: | The proposed model improves on state-of-the-art video multimodal fusion benchmarks. |
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)
Copied to clipboard
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
| Challenge: | SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. |
| Approach: | They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning. |
| Outcome: | The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines. |
From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to fine-grained entity typing are limited by the errors in the annotation process. |
| Approach: | They propose a method that can be used to fine-tune a model to a new type schema without creating distantly labeled data. |
| Outcome: | The proposed approach outperforms state-of-the-art weak supervision based methods under the few-shot setting. |
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers (2023.acl-long)
Copied to clipboard
| Challenge: | toxicity and IMDB review datasets show that pre-trained NLP classifiers learn spurious correlations between input features and label . |
| Approach: | They propose an algorithm to regularize the learnt effect of features on the model’s prediction to the estimated effect of a feature on label. |
| Outcome: | The proposed method minimises spurious correlations and improves minority group accuracy while improving total accuracy compared to standard training. |
What Makes Pre-trained Language Models Better Zero-shot Learners? (2023.acl-long)
Copied to clipboard
| Challenge: | Current methods for prompt learning in zero-shot scenarios rely on a development set with sufficient human-annotated data to select the best-performing prompt template. |
| Approach: | They propose a method for screening reasonable prompt templates in zero-shot text classification using language discrepancy. |
| Outcome: | The proposed method improves prediction performance in a realistic zero-shot setting, eliminating the need for labelled examples. |
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for zero-shot learning are based on in-context training, but performance drops when no demonstrations are available. |
| Approach: | They propose a new method that constructs pseudo-demonstrations for a given test input using a raw text corpus and applies techniques to reduce copying. |
| Outcome: | The proposed method outperforms previous zero-shot methods on nine classification datasets and is on par with in-context learning with labeled training data in the few-shot setting. |
Learning Optimal Policy for Simultaneous Machine Translation via Binary Search (2023.acl-long)
Copied to clipboard
| Challenge: | Simultaneous machine translation model needs a precise translation policy to achieve good latency-quality trade-offs. |
| Approach: | They propose a method for building the optimal translation policy online via binary search by employing explicit supervision. |
| Outcome: | Experiments on four translation tasks show that the proposed method exceeds strong baselines across all latency scenarios. |
Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed. |
| Approach: | They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation. |
| Outcome: | The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set. |
StoryARG: a corpus of narratives and personal experiences in argumentative texts (2023.acl-long)
Copied to clipboard
| Challenge: | Narratives and argumentation are deeply related, according to psychologists and social scientists. |
| Approach: | They annotated StoryARG from well-established corpora in computational argumentation and the Social Sciences, as well as comments to New York Times articles. |
| Outcome: | The dataset contains 2451 textual spans annotated at two levels . it reveals positive impact on effectiveness for stories which illustrate a solution to a problem and in general, annotator-specific preferences . |
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in language modeling have enabled applications across multiple domains such as education, jurisprudence, and healthcare. |
| Approach: | They propose a method to use knowledge to identify which rare words are important and uplift their conditional probability. |
| Outcome: | The proposed approach reduces the uncertainty of the model and improves factuality and coherence without negatively impacting fluency. |
Sequence Parallelism: Long Sequence Training from System Perspective (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on memory-efficient parallelisms to reduce time and space complexity focuses on reducing time and complexity from system perspective. |
| Approach: | They propose a memory-efficient parallelism to reduce time and space complexity . they split input sequence into multiple chunks and feed each chunk into GPU . |
| Outcome: | The proposed approach is compatible with most existing parallelisms and makes 4D parallelismal possible. |
MUSTIE: Multimodal Structural Transformer for Web Information Extraction (2023.acl-long)
Copied to clipboard
Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Zenglin Xu, Shaoliang Nie, Sinong Wang, Madian Khabsa, Hamed Firooz, Dongfang Liu
| Challenge: | Recent sequential modeling approaches focus on extracting information from textual sources while ignoring rich information from other modalities such as image and web layout. |
| Approach: | They propose a novel MUltimodal Structural Transformer that integrates multiple modalities for web information extraction. |
| Outcome: | The proposed model outperforms existing methods on WebSRC and Common Crawl benchmarks. |
Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work on retrieval augmentation fine-tuned the retriever and the LM, making them closely coupled. |
| Approach: | They propose a generic retrieval plug-in that can be used to fine-tune retrieval augmentation and a LM to learn a user's preferences. |
| Outcome: | The proposed retriever improves the generalization of large language models on the MMLU and PopQA datasets by learning LM’s preferences from a known source LM . |
TableVLM: Multi-modal Pre-training for Table Structure Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Tables are useful for displaying data in an organized manner, but they are difficult to extract from images because of their structure, notation, and representation. |
| Approach: | They propose a multi-modal pre-training model for table structure recognition that captures table structure-related features by multiple unsupervised objectives inspired by masked visual-language modeling. |
| Outcome: | The proposed model improves tree-editing-distance-score on ComplexTable by 1.97% . |
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to biomedical relation extraction (RE) are limited due to the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels. |
| Approach: | They propose a method which converts biomedical relation extraction (RE) as natural language inference formulation through indirect supervision. |
| Outcome: | Extensive experiments on three widely-used biomedical RE benchmarks show that indirect supervision improves biomedically relation extraction even when a domain gap exists. |
Dynamic Routing Transformer Network for Multimodal Sarcasm Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for multimodal sarcasm detection rely on fixed architectures to capture cross-modal incongruity. |
| Approach: | They propose a method that uses dynamic paths to activate different routing transformer modules with hierarchical co-attention adapting to cross-modal incongruity. |
| Outcome: | The proposed method is compared to state-of-the-art methods on a public dataset. |
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary (2023.acl-long)
Copied to clipboard
| Challenge: | Dense retrieval models based on text representations have proven very effective, but when applied off-the-shelf they often experience a severe drop in performance. |
| Approach: | They propose to interpret the vector representations produced by dual encoders by projecting them into the model’s vocabulary space. |
| Outcome: | The proposed model significantly improves on the BEIR benchmark and in zero-shot settings. |
Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have achieved competitive performance with limited labeled data for many NLP tasks. |
| Approach: | They propose a prompt-based data selection method for pre-trained language models fine-tuning under cold-start scenarios. |
| Outcome: | The proposed method outperforms the strongest cold-start data selection baselines on six text classification datasets with 128 labels. |
Training-free Neural Architecture Search for RNNs and Transformers (2023.acl-long)
Copied to clipboard
| Challenge: | Neural architecture search (NAS) has allowed for the automatic creation of new and effective neural network architectures. |
| Approach: | They develop a new NAS metric that predicts the trained performance of an RNN architecture and significantly outperforms existing NAS metrics. |
| Outcome: | The proposed metric outperforms existing training-free metrics on the NAS-Bench-NLP benchmark. |
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs (2023.acl-long)
Copied to clipboard
| Challenge: | a large-scale cross-lingual summarization dataset is available for free . a cross-linguistic summarizing model can be trained in any target language . |
| Approach: | They propose a multistage data sampling algorithm to train a cross-lingual summarization model capable of summarizing an article in any target language. |
| Outcome: | The proposed model outperforms baseline models on ROUGE and LaSE. |
Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
| Approach: | They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks. |
| Outcome: | The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training. |
Bi-Phone: Modeling Inter Language Phonetic Influences in Text (2023.acl-long)
Copied to clipboard
Abhirut Gupta, Ananya B. Sai, Richard Sproat, Yuri Vasilevski, James Ren, Ambarish Jash, Sukhdeep Sodhi, Aravindan Raghuveer
| Challenge: | Increasingly, people are forced to use the Web in languages they have low literacy in due to technology asymmetries. |
| Approach: | They propose a method to mine phoneme confusions for pairs of L1 and L2 and plug them into a generative model for synthetically producing corrupted L2 text. |
| Outcome: | The proposed method corrupts the popular language understanding benchmark SuperGLUE and improves performance. |
Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | Current captioning models are limited to the English language due to the largescale paired image-caption datasets. |
| Approach: | They propose to integrate the scene graph (SG) structures and the syntactic constituency trees into a captioner to improve captioning relevancy and fluency. |
| Outcome: | The proposed model improves captioning relevancy and fluency on English-Chinese transfers. |
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. |
| Approach: | They propose a plan-and-solve (PS) prompting that includes a few manual steps to generate reasoning steps and improves the quality of generated reasoning steps. |
| Outcome: | The proposed strategy outperforms Zero-shot-CoT on ten reasoning problems and has comparable performance to 8-shot CoT prompting on the math reasoning problem. |
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for retrieval-oriented language models focus on contextualized embedding of the [CLS] token, but recent study shows that ordinary tokens besides [CLL] may provide extra information, which help to produce a better representation effect. |
| Approach: | They propose a method where all contextualized embeddings of pre-trained model can be jointly pre-trained for retrieval tasks. |
| Outcome: | The proposed method improves the quality of representation where all contextualized embeddings of the pre-trained model can be leveraged. |
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition (2023.acl-long)
Copied to clipboard
| Challenge: | Existing vector-based explanation methods for Transformer-based models are limited in their ability to explain the decisions of multiple layers. |
| Approach: | They propose a vector-based explanation method based on the construction of decomposed token representations and their successive propagation throughout the model without mixing them in between layers. |
| Outcome: | The proposed method outperforms existing vector-based and gradient-based methods on transformer-based models by a wide margin. |
Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step (2023.acl-long)
Copied to clipboard
| Challenge: | Symbolic Chain-of-thought Distillation (SCoTD) is a method to train a smaller student model on rationalizations sampled from a significantly larger teacher model. |
| Approach: | They propose a method to train a smaller student model on rationalizations from a larger teacher model. |
| Outcome: | The proposed method improves the performance of a student model in supervised and few-shot settings and especially for challenge sets. |
Generating EDU Extracts for Plan-Guided Summary Re-Ranking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate summary candidates for re-ranking produce redundant, and often low quality, content. |
| Approach: | They propose a method to generate candidates for re-ranking that addresses these issues by grounding each abstract on its own unique content plan and creating distinct plan-guided abstracts using a model's top beam. |
| Outcome: | The proposed method outperforms baseline decoding methods on CNN, NYT, and Xsum and shows that prompting GPT-3 to follow EDU plans outperformed sampling-based methods by 1.05 points. |
A Survey on Asking Clarification Questions Datasets in Conversational Systems (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on Asking Clarification Questions (ACQs) are incomparable due to inconsistent data, experimental setups and evaluation strategies. |
| Approach: | They analyse the current research status on Asking Clarification Questions (ACQs) and propose a set of evaluation metrics and benchmarks for multiple ACQs-related tasks. |
| Outcome: | The proposed techniques are compared with the available datasets and evaluated against benchmarks. |
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters (2023.acl-long)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). |
| Approach: | They propose to use Chain-of-Thought (CoT) prompting to encourage the LLM to generate intermediate rationales for solving a problem by providing a series of reasoning steps in the demonstrations. |
| Outcome: | The proposed model can generate coherent lines of reasoning even with invalid demonstrations while still generating coherent lines during inference. |
Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation (2023.acl-long)
Copied to clipboard
Jean Maillard, Cynthia Gao, Elahe Kalbassi, Kaushik Ram Sadagopan, Vedanuj Goswami, Philipp Koehn, Angela Fan, Francisco Guzman
| Challenge: | Existing datasets are not economical to create large-scale datasets, but for low-resource languages, a few thousand professionally translated sentence pairs can be useful. |
| Approach: | They propose to use a dataset to train machine translation models on pre-existing and synthetic data to augment them with millions of sentences through backtranslation. |
| Outcome: | The proposed model can cover hundreds of languages with high quality training data even when smaller but lower quality datasets are used. |
RMLM: A Flexible Defense Framework for Proactively Mitigating Word-level Adversarial Attacks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing defenses focus on improving robustness of the victim model in training, but neglect to mitigate adversarial attacks during inference. |
| Approach: | They propose a framework that confuses attackers and corrects adversarial contexts . their framework helps improve the robustness of the victim model during inference . |
| Outcome: | The proposed framework improves the robustness of the victim model in training . it also corrects abnormal contexts in the representation level and filtering out examples . |
Gradient-based Intra-attention Pruning on Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models are computationally expensive and slow in inference due to their large sizes. |
| Approach: | They propose a structured pruning method which combines pruning with knowledge distillation to yield highly effective models. |
| Outcome: | The proposed method outperforms other pruning methods in sparsity regimes while maintaining 93% 99% performance. |
Learning to Substitute Spans towards Improving Compositional Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | despite the rising prevalence of neural sequence models, there is a deficiency in compositional generalization. |
| Approach: | They propose a compositional augmentation strategy that enables multi-grained composition of substructures in the whole training set. |
| Outcome: | The proposed strategy outperforms existing strategies on three compositional generalization benchmarks. |
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context. |
| Approach: | They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals. |
| Outcome: | The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness. |
BREAK: Breaking the Dialogue State Tracking Barrier with Beam Search and Re-ranking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dialogue state tracking still have a JGA of 60% on MultiWOZ 2.1 . break framework provides a simple yet effective way to generate dialogue state candidates . |
| Approach: | They propose a framework that generates k-best dialogue state candidates with beam search and re-ranks them to select the correct dialogue state. |
| Outcome: | The proposed framework pushes the joint goal accuracy to 80-90% on MultiWOZ 2.1-2.4. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
Copied to clipboard
| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to debiase Natural Language Understanding models use dataset biases instead of learning the intended task. |
| Approach: | They propose a debiasing framework that detects and purifies dataset biases using information entropy. |
| Outcome: | The proposed framework improves the stability of performance on out-of-distribution datasets for a set of widely adopted NLU models. |
Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |
Learning to Generate Equitable Text in Dialogue from Biased Training Data (2023.acl-long)
Copied to clipboard
| Challenge: | Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system. |
| Approach: | They propose to use theories of computational learning to study equitable text generation in dialogues using augmented data to prove formal definitions of equity in text generation and formal connections between human-likeness and learning equity. |
| Outcome: | The proposed model predicts relative-performance of multiple algorithms in generating equitable text as measured by human and automated evaluation. |
Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on the hierarchical text classification problem is limited due to the complexity of label hierarchy and intensive labeling cost. |
| Approach: | They propose a path-based few-shot setting and a strict path-basic evaluation metric to further explore few- shot HTC tasks. |
| Outcome: | The proposed framework outperforms those who inject hierarchy through graph encoders on three popular HTC datasets under the few-shot setting. |
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on multimodal abstractive summarization focus on how to use extracted visual features to produce a concise summary given the multimodal data. |
| Approach: | They propose to improve the visual quality of the multimodal abstractive summarization model by capturing summary-oriented visual features. |
| Outcome: | The proposed approach achieves state-of-the-art under 44 languages and is highly effective on high-resource English datasets. |
Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress (2023.acl-long)
Copied to clipboard
| Challenge: | In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. |
| Approach: | They develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts and uses them to predict voting decisions. |
| Outcome: | The proposed model predicts active and passive cosponsorship with an F1-score of 0.88. |
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent reasoning-based models cannot fully figure out complex causal relationships between mentioned entities with external knowledge. |
| Approach: | They propose a Tree structure Reasoning schEmA that constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities. |
| Outcome: | Extensive experiments on two public CRS datasets show the proposed model works. |
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality (2023.acl-long)
Copied to clipboard
Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li
| Challenge: | Current datasets bias in the English language while leaving other languages underexplored. |
| Approach: | They propose a Chinese answer-to-sequence dataset with high quality and large scale . they propose encoding space for two hybrid knowledge resources to convert this task to a graph-totext problem. |
| Outcome: | The proposed method is effective in generating textual descriptions for the Chinese answer-to-sequence dataset. |
Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques (2023.acl-long)
Copied to clipboard
| Challenge: | a new dataset of news articles is presented that covers genre, framing, and persuasion techniques. |
| Approach: | They propose a multilingual multifacet dataset of news articles annotated for genre, framing and persuasion techniques. |
| Outcome: | The proposed dataset contains 1,612 news articles covering recent news on current topics of public interest in six European languages. |
Learning Action Conditions from Instructional Manuals for Instruction Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | a weakly supervised task is proposed to extract mentions of preconditions and postconditions of actions from instructional manuals. |
| Approach: | They propose a task dubbed action condition inference which extracts mentions of preconditions and postconditions of actions from instructional manuals. |
| Outcome: | The proposed approach improves on the existing models, but still far behind human performance. |
StoryWars: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Understanding and generating collaborative stories remains an underexplored area due to the lack of open-domain corpora. |
| Approach: | They propose to use a dataset of 40,000 collaborative stories written by 9,400 different authors from an online platform to generate a multi-task benchmark. |
| Outcome: | The proposed model achieves the best performance on fully-supervised, few-shot, and zero-shot scenarios while achieving the best results on the fully-supervised tasks. |
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models have shown impressive performance in following natural language instructions to solve unseen tasks. |
| Approach: | They propose two strategies to help large language models better leverage task instructions . they propose to remove 60% of tokens from the task definitions while maintaining model performance . |
| Outcome: | The proposed approach achieves 4.2 Rouge-L improvement over 119 unseen test tasks. |
Do PLMs Know and Understand Ontological Knowledge? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on pretrained language models focus mainly on factual knowledge, lacking a systematic probing of ontological knowledge. |
| Approach: | They investigate whether Pretrained Language Models store ontological knowledge and have a semantic un- derstanding of the knowledge rather than rote memorization of the surface form. |
| Outcome: | The proposed models can memorize certain ontological knowledge and perform logical reasoning with given knowledge according to ontological entailment rules. |
CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
| Approach: | They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds. |
| Outcome: | The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications. |
Exploring How Generative Adversarial Networks Learn Phonological Representations (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies in natural language processing (NLP) have demonstrated two generic trends: neural networks dominate language-specific machine learning models; the interpretability of these models is limited that the language representation they learned might not align to human language. |
| Approach: | They propose to use a phonological feature-learning architecture to encode contrastive and non-contrastive nasality in French and English vowels. |
| Outcome: | The proposed architecture encodes contrastive and non-contrastive nasality in French and English vowels. |
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to semantic change analysis are limited in their interpretation power and lack of explanatory power. |
| Approach: | They propose to use specialised Flan-T5 language models to generate a definition for each usage and a specialised word sense model to generate the most prototypical definition. |
| Outcome: | The proposed representations outperform token or usage sentence embeddings in word-in-context semantic similarity judgements and are a promising type of lexical representation for NLP. |
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on interactive semantic parsing relies on human annotations to train a model . prior work relied on human-annotated feedback data, which is prohibitively expensive and not scalable . |
| Approach: | They propose a task of simulating NL feedback for interactive semantic parsing . they propose evaluators to assess the quality of the simulated feedback . |
| Outcome: | The proposed simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. |
InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing image captioning metrics provide a single score to measure caption qualities, which are less explainable and informative. |
| Approach: | They propose an Informative Metric for Reference-free Image Caption evaluation to support this feedback . they propose to provide a text precision score, a vision recall score and an overall quality score . |
| Outcome: | The proposed method improves on existing metrics on multiple benchmarks and compares coarse-grained scores with human judgements. |
An Invariant Learning Characterization of Controlled Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Controlled generation is a problem of creating text that contains stylistic or semantic attributes of interest. |
| Approach: | They propose a distribution shift-based control system that can be used to train a predictor of the desired attribute. |
| Outcome: | The proposed method shows that the most effective predictor should be invariant across multiple text environments. |
HistRED: A Historical Document-Level Relation Extraction Dataset (2023.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) tasks are limited to sentencelevel RE, but are not feasible in real-world applications. |
| Approach: | They propose a bilingual relation extraction model that leverages both Korean and Hanja contexts to predict relations between entities. |
| Outcome: | The proposed model outperforms monolingual baselines on histRED . it supports various self-contained subtexts with different lengths . |
A Critical Evaluation of Evaluations for Long-form Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Long-form question answering (LFQA) is an emerging research area within QA . however, its flexibility poses enormous challenges for evaluation . |
| Approach: | They conduct the first targeted study of the evaluation of long-form answers, covering both human and automatic evaluation practices. |
| Outcome: | The proposed evaluations cover human and automatic evaluations. |
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing techniques to fine-tune pre-trained language models on downstream tasks are inadequate. |
| Approach: | They propose a technique to perturb hidden Transformers representations by enhancing generalization of hidden representations from different layers. |
| Outcome: | The proposed technique outperforms vanilla fine-tuning and enhances generalization of hidden representations from different layers. |
Generating User-Engaging News Headlines (2023.acl-long)
Copied to clipboard
| Challenge: | Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article. |
| Approach: | They propose a framework that incorporates user profiling to generate personalized headlines and a combination of automated and human evaluation methods to determine user preference for personalized headline generation. |
| Outcome: | The proposed framework can generate personalized headlines that meet the needs of a diverse audience. |
Word sense extension (2023.acl-long)
Copied to clipboard
| Challenge: | a long-standing effort in natural language processing has focused on word sense disambiguation, but little has been explored about how word meaning is extended toward new context. |
| Approach: | They propose a framework that partitions a word type into two pseudo-tokens that mark its different senses and infers whether the meaning can be extended to convey the sense denoted by the token. |
| Outcome: | The proposed framework outperforms other models in predicting plausible novel senses for over 7,500 English words. |
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism (2023.acl-long)
Copied to clipboard
| Challenge: | Existing generative models for dialogue use the last hidden state to summarize the history of the dialogue. |
| Approach: | They propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) that summarises the accumulated distribution variations of subsequences and builds a model based on it. |
| Outcome: | The proposed model can improve diversity and relevance of responses on two benchmark datasets. |
Decoding Symbolism in Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing language models can be used to decode symbolism, but they are biased in pre-trained corpora. |
| Approach: | They propose to use language models to decode symbols by re-ranking pre-trained models. |
| Outcome: | The proposed framework shows that pre-trained models can mitigate the bias and improve performance to be on par with human models. |
A Survey on Zero Pronoun Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language. |
| Approach: | They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains . |
| Outcome: | The proposed method and methods are compared to other models and evaluation metrics on different benchmarks. |
We Understand Elliptical Sentences, and Language Models should Too: A New Dataset for Studying Ellipsis and its Interaction with Thematic Fit (2023.acl-long)
Copied to clipboard
| Challenge: | ellipsis is a linguistic phenomenon characterized by the omission of one or more sentence elements. |
| Approach: | They investigated how prototypicality affects the ability of Language Models to handle elliptical sentences . they found that models were better suited to evaluating argument thematic fit . |
| Outcome: | The proposed dataset shows that the models perform better for typical events than for atypical ones in different elliptical contexts. |
MPCHAT: Towards Multimodal Persona-Grounded Conversation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on persona-based dialogue has focused on textual persona that delivers personal facts or personalities, but image modality can reveal the speaker’s personal characteristics and experiences in episodic memory. |
| Approach: | They propose a multimodal persona-based dialogue dataset which extends persona with both text and images to contain episodic memories. |
| Outcome: | The proposed dataset extends persona with text and images to contain episodic memories. |
DOC: Improving Long Story Coherence With Detailed Outline Control (2023.acl-long)
Copied to clipboard
| Challenge: | Detailed Outline Control (DOC) framework improves long-range plot coherence . human evaluations of DOC show it outperforms strong Re3 on plot cohesion, outline relevance and interestingness . |
| Approach: | They propose a Detailed Outline Control framework to improve long-range plot coherence . the detailed outliner creates a more detailed, hierarchically structured outline . they propose doc with a detailed controller to ensure the more detailed outline is respected . |
| Outcome: | The proposed framework outperforms Re3 on plot coherence, outline relevance and interestingness. |
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding (2023.acl-long)
Copied to clipboard
Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng, Qiang Lou, Haizhen Huang, Jian Jiao, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. |
| Approach: | They propose a dual-alignment pre-training framework that incorporates both sentence-level and token-level alignment. |
| Outcome: | The proposed framework improves cross-lingual sentence embedding on three cross-linguistic benchmarks. |
Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)
Copied to clipboard
| Challenge: | Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition. |
| Approach: | They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation. |
| Outcome: | The proposed model can associate the image with relevant texts, providing useful supplementary information for translation. |
FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP (2023.acl-long)
Copied to clipboard
| Challenge: | federated learning (FL) is a promising technique for preserving data privacy . however, there is no work on applying FL to legal NLP . |
| Approach: | They propose to use federated learning to train models in a collaborative way without sharing data . they propose to test the FL benchmark on real-world legal data from Chinese courts . |
| Outcome: | The proposed benchmark combines five legal NLP tasks and one privacy task on Chinese courts. |
A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Parameter-Efficient Tuning (PET) fine-tunes pre-trained language models for downstream tasks, but a large reduction in the number of attackable parameters will greatly affect the effectiveness of backdoor attacks, resulting in backdoor forgetting. |
| Approach: | They propose a gradient control method to consolidate the attack effect by freezing most parameters of the pre-trained model and fine-tuning only a small number of parameters. |
| Outcome: | The proposed method improves sentiment classification and spam detection, and can be applied to different tasks. |
History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for conversational KBQA assume the independence of utterances and model them in isolation. |
| Approach: | They propose a History Semantic Graph Enhanced KBQA model that models long-range semantic dependencies in conversation history while maintaining low computational cost. |
| Outcome: | The proposed model outperforms baselines on a widely used question type dataset. |
From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to construct entailment graphs suffer from severe sparsity issues due to limited corpora and the long-tail phenomenon of predicate distributions. |
| Approach: | They propose a multi-stage method to generate entailment graphs by generating new predicates and detecting enanglement relations among seed predicats. |
| Outcome: | The proposed method can generate high-quality graphs with high precision over state-of-the-art methods and boost the performance of down-stream inference tasks. |
Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs . |
| Approach: | They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers. |
| Outcome: | The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets. |
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model (2023.acl-long)
Copied to clipboard
| Challenge: | Neural Machine Translation models are based on a Mixture of Experts architecture and can be pruned to remove up to 80% of experts without further finetuning. |
| Approach: | They propose a pruning method that removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
| Outcome: | The proposed pruning method removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks. |
| Approach: | They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use. |
| Outcome: | The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models. |
From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Current models for natural language understanding require a preprocessing step to convert raw text into discrete tokens. |
| Approach: | They propose a hierarchical open-vocabulary language model that adopts a shallow Transformer architecture to learn word representations from their characters and a deep inter-word Transformer module that contextualizes each word representation by attending to the entire word sequence. |
| Outcome: | The proposed model outperforms baselines on various downstream tasks and is robust to textual corruption and domain shift. |
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Using publicly available materials science text data, we construct a benchmark for evaluating the performance of natural language processing (NLP) models on materials science texts. |
| Approach: | They propose a natural language benchmark for evaluating the performance of natural language processing (NLP) models on materials science text. |
| Outcome: | The proposed model outperforms BERT-based models on scientific text and a model pretrained on materials science journals. |
Code4Struct: Code Generation for Few-Shot Event Structure Prediction (2023.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) trained on a mixture of text and code have demonstrated impressive capability in translating natural language (NL) into structured code. |
| Approach: | They propose to use programming language (PL) inheritance and type annotations to translate text into code to tackle structured prediction tasks. |
| Outcome: | The proposed model outperforms existing models on 20-shot data by 29.5% absolute F1. |
GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles (2023.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarking datasets for Event Argument Extraction (EAE) cover less than 40 event types and 25 entity-centric argument roles. |
| Approach: | They propose to use a large and diverse EAE ontology to create a semantic role labeling dataset for EAE that incorporates 115 events and 220 argument roles. |
| Outcome: | The proposed ontology concludes with 115 events and 220 argument roles, with a significant portion of roles not being entities. |
Efficient Semiring-Weighted Earley Parsing (2023.acl-long)
Copied to clipboard
| Challenge: | Using Earley's context-free parsing algorithm, we show that the speed-ups are effective in practice. |
| Approach: | They propose a context-free parsing algorithm with various known and new speed-ups that improve Earley's (1970) O(N3|G||R|) They also propose 'a binarized version' that achieves runtime of O(M| |G| when the grammar is represented compactly as a single finite-state automaton M. |
| Outcome: | The proposed algorithm can be used to reduce the complexity of CKY on a binarized version of the grammar G. |
Tree-Based Representation and Generation of Natural and Mathematical Language (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for generating and modeling mathematical language are limited . existing models for modeling and generating mathematical language simply treat mathematical expressions as text . |
| Approach: | They propose to combine mathematical expressions and text-based models to generate mathematically valid expressions. |
| Outcome: | The proposed model outperforms baselines on mathematical expression generation tasks. |
ParaLS: Lexical Substitution via Pretrained Paraphraser (2023.acl-long)
Copied to clipboard
| Challenge: | Lexical substitution (LS) is an extremely powerful technology that can be used as a backbone of various NLP applications such as writing assistance. |
| Approach: | They propose two simple decoding strategies that focus on the variations of the target word during decoding to generate substitutes from a paraphraser. |
| Outcome: | The proposed methods outperform state-of-the-art LS methods based on pre-trained language models on three benchmarks. |
Peer-Label Assisted Hierarchical Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to hierarchical text classification focus on parent-child relationships . however, some texts with a category hierarchy also have latent relevancy among labels in the same level of the hierarchy. |
| Approach: | They propose a method to analyze latent relevancy of peer labels and a sample importance learning method to ameliorate the side effects. |
| Outcome: | The proposed method improves the latent relevancy of peer labels on standard datasets. |
Free Lunch for Efficient Textual Commonsense Integration in Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent years have witnessed the emergence of textual commonsense knowledge bases, aimed at providing more nuanced and context-rich knowledge. |
| Approach: | They propose to group training samples with similar commonsense descriptions into a single batch and reuse the encoded description across multiple samples. |
| Outcome: | The proposed method reduces the computational cost while preserving performance on larger datasets and on devices with more memory capacity. |
A Probabilistic Framework for Discovering New Intents (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for detecting unknown intents do not explore the intrinsic structure of unlabeled data. |
| Approach: | They propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. |
| Outcome: | The proposed framework can be used to discover intents with latent variables . it can be applied to three challenging real-world datasets . |
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset (2023.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) is a fundamental task in information extraction, but its extension to multilingual settings is hindered by the lack of supervised resources comparable in size to large English datasets. |
| Approach: | They propose a dataset to analyze relation extraction (RE) in multilingual settings . they find machine translation is a viable strategy to transfer RE instances . |
| Outcome: | The proposed dataset covers 12 typologically diverse languages from 9 language families and is compared with existing datasets. |
Towards Higher Pareto Frontier in Multilingual Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Pareto optimization approaches are limited by the long-tailed distribution of multilingual corpora. |
| Approach: | They propose a Pareto mutual distillation framework that pushes the Paret frontier outwards rather than making trade-offs. |
| Outcome: | The proposed framework pushes the Pareto frontier outwards rather than making trade-offs, the authors show. |
Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization (2023.acl-long)
Copied to clipboard
| Challenge: | Large pre-trained language models (PLMs) have shown remarkable performance in various natural language processing tasks, outperforming small PLMs by a large margin. |
| Approach: | They propose to scale up parameters of pre-trained language models only during fine-tuning to benefit from over-parameterization. |
| Outcome: | The proposed approach can significantly boost the fine-tuning performance of small PLMs and even help small PDMs outperform 3 parameterized larger ones. |
Entity Tracking in Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on the ability of large language models to track discourse entities have not been conducted. |
| Approach: | They propose to investigate whether large language models can track entities . they first investigate whether Flan-T5, GPT-3 and GPT-3.5 can track the state of entities based on an English description of the initial state and a series of state-changing operations. |
| Outcome: | The proposed task investigates whether language models can track entities based on language descriptions and state-changing operations. |
A Textual Dataset for Situated Proactive Response Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Recent data-driven conversational models can return fluent, consistent, and informative responses to many kinds of requests and utterances in task-oriented scenarios. |
| Approach: | They propose a task of proactive response selection based on situational information and a dataset of 1.7k English conversation examples that include situational background information and for each conversation a set of responses. |
| Outcome: | The proposed model can only provide fluent, consistent, and informative responses to a set of 1.7k English conversation examples and is not easy to perform for strong neural models. |
DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks. |
| Approach: | They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans. |
| Outcome: | The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets. |
WACO: Word-Aligned Contrastive Learning for Speech Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing ST methods perform poorly when only a limited amount of parallel data are available for training. |
| Approach: | They propose a Word-Aligned COntrastive learning method for low-resource speech-to-text translation that bridges word-level representations for both speech and text modalities via contrastive learning. |
| Outcome: | The proposed method outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. |
Cross-lingual Continual Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing multi-lingual representations such as the one-hop transfer learning pipeline are difficult to adapt to new languages. |
| Approach: | They propose a cross-lingual continuum learning paradigm that evaluates continuous learning approaches that adapt to emerging data from different languages. |
| Outcome: | The proposed model can be used to adapt to new languages in a sequential manner. |
Faithful Question Answering with Monte-Carlo Planning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to answer questions using large language models lack the ability to faithfully follow the intermediate reasoning steps from the known premises to the answer. |
| Approach: | They propose a faithful question-answering task that uses a Monte-Carlo planning algorithm to produce faithful reasoning steps from the known premises to the answer. |
| Outcome: | The proposed task can produce valid and faithful reasoning steps compared with large language models with a much smaller model size. |
Unbalanced Optimal Transport for Unbalanced Word Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | Figure 1 illustrates the challenges of monolingual word alignment. |
| Approach: | They propose to use the family of optimal transport (OT) to achieve unbalanced word alignment that values alignment and null alignment on unsupervised datasets. |
| Outcome: | The proposed methods are competitive against the state-of-the-art methods on challenging datasets with high null alignment frequencies. |
Guiding Computational Stance Detection with Expanded Stance Triangle Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that strategically-enriched data can significantly improve the performance on out-of-domain and cross-target evaluation. |
| Approach: | They propose to decompose a stance detection task from a theoretical perspective and extend it with additional annotations. |
| Outcome: | The proposed task improves performance on out-of-domain and cross-target evaluations using a linguistic framework. |
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research shows that a multilingual pre-trained language model fine-tuned with one (source) language performs well on downstream tasks for non-source languages . However, there is a clear performance gap between the source and non-sourced languages - this gap can be reduced by reducing forgetting. |
| Approach: | They propose a method to fine-tune a multilingual pre-trained language model fine- tuned with one (source) language and four training policies to address the performance gap. |
| Outcome: | The proposed method outperforms baselines on the XNLI dataset by a clear margin. |
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance. |
| Approach: | They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER) |
| Outcome: | The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs. |
MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks (2023.acl-long)
Copied to clipboard
| Challenge: | Vision and language models exploit unrobust indicators in individual modalities instead of focusing on relevant information in each modality. |
| Approach: | They propose a performance-agnostic multimodality score based on Shapley values that quantifies in which proportions a multimodal model uses individual modalities. |
| Outcome: | The proposed model can quantify in which proportions a multimodal model uses individual modalities for different tasks and datasets. |
Towards Boosting the Open-Domain Chatbot with Human Feedback (2023.acl-long)
Copied to clipboard
| Challenge: | Existing frameworks for pre-training open-domain dialogue models with social media comments generate coherent replies but have difficulties producing engaging responses. |
| Approach: | They propose a framework to boost the open-domain chatbot by leveraging human feedback and annotating the model's candidate responses. |
| Outcome: | The proposed framework boosts the open-domain chatbot by leveraging human demonstrated responses and leveraging the implicit preference in the data collection process. |
Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show the superiority of a mixed-initiative framework for emotional support conversation (ESC) ESC systems are emerging to provide prompt and convenient emotional support for helpseekers, including mental health support, counseling or motivational interviewing. |
| Approach: | They propose a knowledge-enhanced mixed-initiative framework that retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. |
| Outcome: | The proposed framework retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. |
UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Information Extraction (IE) tasks have been solved with different models because of their output structures. |
| Approach: | They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix. |
| Outcome: | The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets. |
Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model (2023.acl-long)
Copied to clipboard
Ali Omrani, Alireza Salkhordeh Ziabari, Charles Yu, Preni Golazizian, Brendan Kennedy, Mohammad Atari, Heng Ji, Morteza Dehghani
| Challenge: | Existing methods for mitigating bias require social-group-specific word pairs for each social attribute (e.g., gender) Existing approaches require only one social attribute, rendering them impractical and costly . |
| Approach: | They propose that stereotype content models capture the underlying connection between bias and stereotypes by embedding only two psychological dimensions of warmth and competence. |
| Outcome: | The proposed method performs comparably to group-specific debiasing on multiple bias benchmarks, but has theoretical and practical advantages over existing methods. |
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation (2023.acl-long)
Copied to clipboard
Yixin Liu, Alex Fabbri, Pengfei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
| Challenge: | Existing studies for summarization evaluation exhibit low inter-annotator agreement or lack scale. |
| Approach: | They propose a modified summarization salience protocol based on fine-grained semantic units and a robust summarizing evaluation benchmark. |
| Outcome: | The proposed protocol is based on fine-grained semantic units and allows for high inter-annotator agreement. |
FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that large language models that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. |
| Approach: | They present a dataset of game play sessions from real D&D gameplay on Discord with true game state info. |
| Outcome: | The proposed model can generate executable Avrae commands, especially after fine tuning. |
A fine-grained comparison of pragmatic language understanding in humans and language models (2023.acl-long)
Copied to clipboard
| Challenge: | Pragmatics and non-literal language understanding are essential to human communication . a long-standing challenge for artificial language models is to capture pragmatics . |
| Approach: | They compare language models and humans on seven pragmatic phenomena using curated English materials. |
| Outcome: | The proposed model achieves high accuracy and matches human error patterns . the results suggest pragmatic behaviors can emerge in models without explicit representations of mental states . |
Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing QA models rely on shortcuts to provide the true answer, referred to as disconnected reasoning problem. |
| Approach: | They propose a causal-effect approach that exploits true multi-hop reasoning instead of shortcuts. |
| Outcome: | The proposed method achieves 5.8% higher points of its Supps score on hotpotQA through true multihop reasoning. |
Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to remove unwanted stereotypical associations from pretrained language models (PLMs) are often focused on removing unwanted stereotypes from PLMs. |
| Approach: | They propose a framework to remove unwanted stereotypical associations in pretrained language models . they propose bias-relevant factors are causal, while labelrelevant factors causal . |
| Outcome: | The proposed framework reduces stereotypical associations after PLMs are fine-tuned . the proposed framework mitigates bias from a causal invariant perspective . |
Parameter-Efficient Fine-Tuning without Introducing New Latency (2023.acl-long)
Copied to clipboard
| Challenge: | Parameter-efficient fine-tuning of pre-trained language models has been demonstrated to be effective, but its inherent characteristics limit its performance. |
| Approach: | They propose to generate a sparse mask in a task-agnostic manner by modifying only a small subset of existing parameters and adding new parameters. |
| Outcome: | The proposed method surpasses existing methods on the GLUE benchmark by a significant margin. |
MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task that aims at classifying mention spans into entity types. |
| Approach: | They propose a variational memory-augmented few-shot named entity recognition model that uses a memory module to store information from source domain and retrieve relevant information from the memory to augment few-shot task in target domain. |
| Outcome: | The proposed model can adapt the learned knowledge from source domain to target domain and achieve superior performance on English and Chinese cross domain few-shot NER datasets. |
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages (2023.acl-long)
Copied to clipboard
Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, Prem Natarajan
| Challenge: | We present the MASSIVE dataset–Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant evaluation. |
| Approach: | They present a 1M-example dataset of Amazon Slu utterances . they localize the dataset into 50 typologically diverse languages . |
| Outcome: | The proposed model includes exact match accuracy, intent classification accuracy, and slot-filling F1 score. |
Distilling Script Knowledge from Large Language Models for Constrained Language Planning (2023.acl-long)
Copied to clipboard
Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Jankowski, Yanghua Xiao, Deqing Yang
| Challenge: | Existing work exploits language models to plan for abstract goals of stereotypical activities, but leaves more specific goals with multi-facet constraints understudied. |
| Approach: | They propose an over-generate-then-filter approach to improve large language models on constrained language planning task by distilling a constrained script dataset. |
| Outcome: | The proposed approach improves the constrained language planning ability of large language models on constraint faithfulness and also in smaller LMs. |
REDFM: a Filtered and Multilingual Relation Extraction Dataset (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Relation Extraction models rely on small datasets with low coverage of relation types . current systems rely only on small data sets with limited coverage of relationship types - especially when working with languages other than english. |
| Approach: | They propose to use an automatic annotated dataset to train relation extraction systems. |
| Outcome: | The proposed model can extract triplets in multiple languages from a human-revised dataset. |
Modeling Appropriate Language in Argumentation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on offensive language has not been systematically addressed in debates . a new taxonomy of 14 dimensions determines inappropriate language in online discussions . |
| Approach: | They propose a taxonomy of 14 dimensions that determine inappropriate language in online discussions . they build on arguments quality corpora and annotate them on a corpus of 2191 arguments . |
| Outcome: | The proposed taxonomy covers the concept of appropriateness comprehensively, showing plausible correlations with argument quality dimensions. |
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels (2023.acl-long)
Copied to clipboard
| Challenge: | Utilizing language models without internal access is becoming an attractive paradigm in the field of NLP . prompting has shown progressive performance enhancements in situations where data labels are scarce or unavailable. |
| Approach: | They propose a method that uses a weak-supervision signal to train a lightweight model without internal access to data labels. |
| Outcome: | The proposed method improves text classification accuracy with weak-supervision signal without accessing weights or gradients of the LM model or data labels. |
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies predict sentiment elements in a fixed order, which ignores the interdependence of the elements and the diversity of language expression. |
| Approach: | They propose a multi-view process that aggregates sentiment elements generated in different order . they use element order prompts to guide the language model to generate multiple tuples with different element order based on a given text . |
| Outcome: | The proposed method outperforms existing methods on 10 datasets of 4 benchmark tasks and is highly flexible and transferable across tasks. |
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
Copied to clipboard
| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
Explanation-based Finetuning Makes Models More Robust to Spurious Cues (2023.acl-long)
Copied to clipboard
Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
| Challenge: | Large Language Models (LLMs) learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data. |
| Approach: | They propose an explanation-based approach to fine tune large language models to generate a free-text explanation supporting their answer. |
| Outcome: | The proposed model is more robust against spurious cues in terms of accuracy drop across four classification tasks: ComVE (+1.2), CREAK (+9.1), e-SNLI (+5.4), and SBIC (+6.5). |
CAME: Confidence-guided Adaptive Memory Efficient Optimization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing memory-efficient methods require second-moment estimates of the per-parameter gradients to maintain their performance. |
| Approach: | They propose to use memory-efficient optimizers to reduce memory usage by preserving second-moment estimates of gradients. |
| Outcome: | The proposed method achieves fast convergence and lower memory usage across training tasks. |
On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work has focused on logical reasoning tasks; it remains unclear whether improvements hold for more diverse types of reasoning, especially in socially situated contexts. |
| Approach: | They perform a controlled evaluation of zero-shot CoT reasoning in two socially sensitive domains: harmful questions and stereotype benchmarks. |
| Outcome: | The results show that zero-shot CoT reasoning increases model’s likelihood to produce harmful or undesirable output, but decreases with improved instruction following. |
Solving Math Word Problems via Cooperative Reasoning induced Language Models (2023.acl-long)
Copied to clipboard
Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Yongfeng Huang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang
| Challenge: | Large-scale pre-trained language models (PLMs) can be used to solve math word problems, but they lack fast adaptivity as humans. |
| Approach: | They propose a cooperative reasoning-induced PLM for solving the math word problem . they use system 1 as the generator and system 2 as the verifier to generate reasoning paths . |
| Outcome: | The proposed model improves on several mathematical reasoning datasets and achieves 9.6% improvement over baselines. |
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work has explored using sequence-to-sequence rewriting models to transform biased outputs into more gender-fair language by creating pseudo training data through linguistic rules. |
| Approach: | They propose to use machine translation models to create gender-biased text from real gender-fair text via round-trip translation to eliminate rule-based data creation. |
| Outcome: | The proposed approach matches the performance of state-of-the-art rewriting models for English. |
Early Discovery of Disappearing Entities in Microblogs (2023.acl-long)
Copied to clipboard
| Challenge: | a study on detecting disappearing entities from noisy microblogs has been published on the real world . a major challenge is detecting uncertain contexts of disappearing entity from noisy posts . |
| Approach: | They propose to use Twitter to detect disappearing entities from noisy microblogs . they build large-scale Twitter datasets of disappearing entity and refine word embeddings based on these data . |
| Outcome: | The proposed method outperforms baseline methods on noisy microblog streams and more than 70% of disappearing entities in Wikipedia are discovered earlier than the update on Wikipedia. |
DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing generative masked language models have a shared training objective, i.e., denoising. |
| Approach: | They propose a noise schedule for the forward diffusion process that controls the degree of noise added at each step based on the information of each token. |
| Outcome: | The proposed model improves on existing models in terms of perplexity and BLEU score. |
Lifting the Curse of Capacity Gap in Distilling Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that pretrained language models require a tremendous amount of inference compute to perform. |
| Approach: | They propose to compress pretrained language models to small ones with a teacher-student paradigm to fill the capacity gap. |
| Outcome: | The proposed model achieves state-of-the-art performance at small FLOPs compared with competitive baselines. |
Towards Faithful Dialogues via Focus Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge-grounded models rely on elaborate data engineering or increasing the model’s parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration. |
| Approach: | They propose a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss. |
| Outcome: | The proposed approach achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability. |
Back Translation for Speech-to-text Translation Without Transcripts (2023.acl-long)
Copied to clipboard
| Challenge: | End-to-end speech-totext translation (ST) is often achieved by utilizing source transcripts, but transcripts are only sometimes available since numerous unwritten languages exist worldwide. |
| Approach: | They propose an algorithm to synthesize pseudo ST data from monolingual target data to enhance ST without generating source transcripts. |
| Outcome: | The proposed method achieves an average boost of 2.3 BLEU on MuST-C En-De, En-Fr, and En-Es datasets. |
Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation (2023.acl-long)
Copied to clipboard
| Challenge: | Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem, but it has yet to be applied to the zero-shot domain adaptation. |
| Approach: | They propose to use descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer’s self-attention mechanism. |
| Outcome: | The proposed method outperforms previous methods on the MultiWOZ and SGD benchmarks. |
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing graph neural networks (GNNs) teach message passing on a graph from text, resulting in a semantic gap between graph knowledge and text. |
| Approach: | They propose a framework to integrate external graph knowledge into chatbots by coagulating representations of both text and graph knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) baselines on dialogue generation. |
Multi-modal Action Chain Abductive Reasoning (2023.acl-long)
Copied to clipboard
Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu
| Challenge: | Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena. |
| Approach: | They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer. |
| Outcome: | The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset. |
Exploring the Capacity of Pretrained Language Models for Reasoning about Actions and Change (2023.acl-long)
Copied to clipboard
| Challenge: | Recent transformer-based language models (LMs) provide reasoning over textual benchmarks . RAC is essential to understand and interact with the ever-changing environment . |
| Approach: | They propose to use a transformer-based language model to learn to reason over textual benchmarks. |
| Outcome: | The proposed model minimizes the influence of other linguistic requirements to focus on RAC. |
Unified Demonstration Retriever for In-Context Learning (2023.acl-long)
Copied to clipboard
Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu
| Challenge: | In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. |
| Approach: | They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback. |
| Outcome: | The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains. |
Movie101: A New Movie Understanding Benchmark (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to narrate movies with no actors are difficult to implement in real situations . a new metric is proposed to provide the best correlation with human evaluation . |
| Approach: | They propose a large-scale Chinese movie benchmark to help visually impaired enjoy movies . they propose metric called Movie Narration Score (MNScore) which achieves best correlation with human evaluation. |
| Outcome: | The proposed method outperforms baselines and the existing methods. |
Enhancing Language Representation with Constructional Information for Natural Language Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in natural language processing focus on acquiring lexico-semantic information. |
| Approach: | They propose a construction grammar which highlights the pairings of form and meaning to enrich language representation. |
| Outcome: | The proposed model is superior to existing models on a variety of NLU tasks. |
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for inductive reasoning over knowledge graphs lack the ability to model the logical structures of complex queries. |
| Approach: | They propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs that encodes linearized query structures and entities using pre-trained language models to find answers. |
| Outcome: | The proposed framework encodes query structures and entities using pre-trained language models to find answers. |
DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models that describe concepts in everyday situations are difficult to summarize in a single sentence. |
| Approach: | They propose DimonGen, which generates sentences describing concept relationships in everyday scenarios. |
| Outcome: | The proposed model outperforms baseline models in terms of quality and diversity of generated sentences. |
Incorporating Attribution Importance for Improving Faithfulness Metrics (2023.acl-long)
Copied to clipboard
| Challenge: | Feature attribution methods (FAs) are popular for providing insights into the model reasoning process of making predictions. |
| Approach: | They propose a simple yet effective criterion that randomly masks tokens proportionately to their FA importance. |
| Outcome: | The proposed method is more faithful than hard sufficiency and comprehensiveness metrics. |
Reward Gaming in Conditional Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has used reward functions learned from human annotations to align conditional text generation models with desired behaviors. |
| Approach: | They propose to use reinforcement learning to train conditional text generation models with reward functions learned from human annotations to align outputs with desired behaviors. |
| Outcome: | The proposed framework improves the quality of generated summaries by using saliency and faithfulness metrics. |
Hidden Schema Networks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models that encode rich semantic and syntactic content are biased, but they are effective at encoding symbolic representations. |
| Approach: | They propose a neural language model that enforces explicit relational structures which allow for compositionality onto the output representations of pretrained language models. |
| Outcome: | The proposed model can encode sentences into sequences of symbols and infer the posterior distribution of the model from natural language datasets. |
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations (2023.acl-long)
Copied to clipboard
| Challenge: | Using hidden representations, pretrained language models are prone to overfitting due to the huge amount of parameters. |
| Approach: | They propose a method that inserts random autoencoders between hidden layers of a PLM to transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers. |
| Outcome: | The proposed method improves performance across sequence- and token-level lowresource tasks. |
An Ordinal Latent Variable Model of Conflict Intensity (2023.acl-long)
Copied to clipboard
| Challenge: | Advances in automated event extraction yield massive data sets of “who did what to whom” micro-records that enable data-driven approaches to monitoring conflict. |
| Approach: | They propose a probabilistic generative model that assumes each observed event is associated with a latent intensity class. |
| Outcome: | The proposed model obtains comparatively good held-out predictive performance on a conflictual to cooperative scale. |
Multilingual Conceptual Coverage in Text-to-Image Models (2023.acl-long)
Copied to clipboard
| Challenge: | Neural text-to-image systems generate coherent, visually-appealing images with novel combinations of objects, scenarios, and styles. |
| Approach: | They propose a technique to benchmark the degree to which a generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns. |
| Outcome: | The proposed technique can be used to benchmark T2I models in terms of multilinguality and identify model-specific weaknesses, spurious correlations, and biases without a-priori assumptions. |
Pre-Training to Learn in Context (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models are not explicitly trained to learn in context. |
| Approach: | They propose a framework to enhance in-context learning by pre-training language models on a large collection of "intrinsic tasks" they evaluate the in-constitution learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark . |
| Outcome: | The proposed framework outperforms larger language models with nearly 4x parameters on seven widely-used datasets and the Super-NaturalInstrctions benchmark. |
Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers (2023.acl-long)
Copied to clipboard
| Challenge: | In recent years, machine translation has become very successful for high-resource language pairs. |
| Approach: | They conduct interviews with community leaders, teachers, and language activists to shed light on ethical considerations for the automatic translation of Indigenous languages. |
| Outcome: | The results show that the inclusion of native speakers and community members is vital to performing better and more ethical research on Indigenous languages. |
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating. |
| Approach: | They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages. |
| Outcome: | The proposed benchmark outperforms pre-trained models in Russian in zero-shot cross-lingual transfer to low-resource languages. |
Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments (2023.acl-long)
Copied to clipboard
| Challenge: | Existing language models lack grounding to real-world environments . a missing piece is the connection between LMs and the environment . |
| Approach: | They propose a generic framework for grounded language understanding that capitalizes on discriminative ability of LMs instead of their generative ability. |
| Outcome: | The proposed framework capitalizes on discriminative ability of LMs instead of their generative ability. |
Privacy-Preserving Domain Adaptation of Semantic Parsers (2023.acl-long)
Copied to clipboard
| Challenge: | Task-oriented dialogue systems often assist users with personal or confidential matters . a lack of privacy controls prevents developers from observing actual usage . authors propose a method to generate realistic user utterances synthetically without compromising privacy . |
| Approach: | They propose a method which generates latent semantic parses and generates utterances based on the parses. |
| Outcome: | The proposed method improves MAUVE by 2.5X and parse tree function-type overlap by 1.3X . it also shows gains of 8.5% points on its accuracy with the new feature . |
Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)
Copied to clipboard
Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi
| Challenge: | Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology. |
| Approach: | They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement . |
| Outcome: | The proposed method outperforms the state-of-the-art models on three benchmarks. |
Actively Supervised Clustering for Open Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Open Relation Extraction (OpenRE) use a two-stage pipeline, which learns relation representations and assignments in the first stage, then manually labels relation for each cluster. |
| Approach: | They propose a method that performs relation learning and relation labeling simultaneously without a significant increase in human effort. |
| Outcome: | The proposed method improves existing SOTA methods by 13.8% and 10.6% on two datasets. |
ConvGQR: Generative Query Reformulation for Conversational Search (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to determine a good search query from the whole conversation context are expensive and often lead to sub-optimal results. |
| Approach: | They propose a framework to reformulate conversational queries based on generative pre-trained language models (PLMs) they propose generative knowledge infusion mechanism to optimize query reformulation and retrieval. |
| Outcome: | Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR. |
KILM: Knowledge Injection into Encoder-Decoder Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Large pre-trained language models retain implicit knowledge within their parameters, but are susceptible to memorizing the pretraining corpora rather than capturing the knowledge within them. |
| Approach: | They propose to inject entity-related knowledge into encoder-decoder PLMs via a generative knowledge infilling objective through continued pre-training. |
| Outcome: | The proposed approach outperforms state-of-the-art models on general NLU and NLG tasks while maintaining their original performance. |
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions (2023.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for video-grounded dialogues neglect the intrinsic attributes of multimodal dialogues, such as scene and topic transitions. |
| Approach: | They propose to use a large scale video-grounded scene&topic AwaRe dialogue dataset to study video-based dialogue understanding. |
| Outcome: | The proposed dataset shows that multimodal information and segments are important in video-grounded dialogue understanding and generation. |
NLPeer: A Unified Resource for the Computational Study of Peer Review (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies of peer review for scholarly publications lack datasets and multi-domain corpora to support this complex process. |
| Approach: | They propose to use NLPeer to build a multi-domain corpus of more than 5k papers and 11k review reports from five different venues to support reviewers. |
| Outcome: | The proposed datasets and analysis of three review assistance tasks include a guided skimming task. |
IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures (2023.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks only evaluate model performance on tables with explicit table structures, which means headers are explicitly annotated and treated as model input during inference. |
| Approach: | They propose a new Table Question Answering (TQA) dataset with implicit and multi-type table structures that requires the model to understand tables without directly available header annotations. |
| Outcome: | The proposed framework outperforms baselines on a dataset with implicit and multi-type table structures and can handle multi-table tables including previously neglected complex tables. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
Copied to clipboard
Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models’ Memories (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain. |
| Approach: | They propose to decouple the feed-forward networks of the Transformer architecture into two parts to maintain old-domain knowledge and a mixture-of-adapters gate to inject domain-specific knowledge in parallel. |
| Outcome: | The proposed method achieves superior performance on in-domain, out-of-domain and knowledge-intensive tasks. |
Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing unsupervised approaches for learning knowledge graphs require multiple modules and require entity information or relation type for training. |
| Approach: | They propose a method that uses a unified pretrained language model to achieve fully unsupervised graph-text mutual conversion for the first time. |
| Outcome: | The proposed method outperforms state-of-the-art methods for G2T and T2G tasks by fine-tuning only one pretrained model. |
Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications (2023.acl-long)
Copied to clipboard
| Challenge: | Large-scale pre-trained language models are brittle against specifically crafted adversarial examples, leading to increasing interest in probing the adversariality of NLP systems. |
| Approach: | They propose a two-stage framework that combines randomized smoothing and masked inference to improve the adversarial robustness of NLP systems. |
| Outcome: | The proposed framework improves adversarial robustness by 2 to 3 times over existing state-of-the-art methods on benchmark datasets. |
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes (2023.acl-long)
Copied to clipboard
| Challenge: | Existing learned metrics perform unsatisfactory across text generation tasks or require human annotations for training on specific tasks. |
| Approach: | They propose a self-supervised approach to train a model-based metric for text generation evaluation using sentences retrieved from a corpus. |
| Outcome: | The proposed model outperforms all prior unsupervised metrics on four text generation evaluation benchmarks, with an average Kendall improvement of 0.158. |
Tokenization and the Noiseless Channel (2023.acl-long)
Copied to clipboard
| Challenge: | Subword tokenization is a key part of most NLP pipelines, but little is known about why some combinations lead to improved downstream model performance. |
| Approach: | They propose that good tokenizers lead to efficient channel usage . they propose that an optimal encoding assigns extremely long codes to low-frequency subwords . |
| Outcome: | The proposed tokenizers have a very strong correlation with BLEU in machine translation . the proposed function can be used to improve model performance in the downstream task . |
Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers (2023.acl-long)
Copied to clipboard
| Challenge: | a novel chart-based method for extracting parse trees from masked language models is proposed . a graph-based approach can be used to extract parser trees without training separate parsers . |
| Approach: | They propose a chart-based method for extracting parse trees from masked language models . they use a set of perturbations motivated by the linguistic concept of constituency tests to score each span . |
| Outcome: | The proposed method outperforms state-of-the-art methods on english with masked LMs and in multilingual settings. |
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for misinformation detection are limited by data scarcity . existing methods fail to detect early-stage misinformation on emerging topics . |
| Approach: | They propose a meta learning based approach for domain adaptive few-shot misinformation detection that leverages limited target examples to provide feedback and guide the knowledge transfer from the source to the target domain. |
| Outcome: | The proposed method improves performance on real-world datasets with reduced parameters. |
Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity. |
| Approach: | They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space. |
| Outcome: | The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets. |
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective (2023.acl-long)
Copied to clipboard
Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Existing efforts to detect commonsense causation from the causal inference perspective are inadequate to seize commonsensical causations. |
| Approach: | They propose a task to detect commonsense causation between two events in context . they propose 'contextualized commons sense causal reasoning' framework that uses covariates to remove confounding effects . |
| Outcome: | The proposed framework can detect commonsense causality more accurately than baselines. |
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization (2023.acl-long)
Copied to clipboard
| Challenge: | Besides digital archiving of memes and their metadata, there is no efficient way to deduce a meme’s context dynamically. |
| Approach: | They propose a task to mine the context that succinctly explains the background of a meme and a related document to capture cross-modal semantic dependencies between the meme and the context. |
| Outcome: | The proposed dataset outperforms existing systems and shows that it can capture cross-modal semantic dependencies between the meme and the context. |
WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Answering non-factoid questions (NFQs) is a challenging task, requiring passage-level answers that are difficult to construct and evaluate. |
| Approach: | They propose a multi-document NFQA benchmark built on WikiHow, a website dedicated to answering “how-to” questions. |
| Outcome: | The proposed framework includes 11,746 human-written answers along with 74,527 supporting documents. |
Making Language Models Better Reasoners with Step-Aware Verifier (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models have made impressive progress in few-shot learning but still face difficulties in reasoning tasks such as GSM8K. |
| Approach: | They propose a new approach that uses a verifier to filter out incorrect answers based on a weighted voting scheme to improve reasoning ability of language models. |
| Outcome: | The proposed approach improves GSM8K reasoning rate by 17.9% to 58.1%. |
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations (2023.acl-long)
Copied to clipboard
| Challenge: | Discourse markers are natural representations of discourse in our daily language. |
| Approach: | They propose to use unlimited discourse marker data to learn a Distributed Marker Representation by bridging markers with sentence pairs. |
| Outcome: | The proposed model outperforms existing models on the implicit discourse relation recognition task and provides strong interpretability. |
MISGENDERED: Limits of Large Language Models in Understanding Pronouns (2023.acl-long)
Copied to clipboard
| Challenge: | excluding non-binary gender identities can perpetuate harm against non-bisexual individuals through exclusion and marginalization. |
| Approach: | They propose a framework for evaluating large language models’ ability to correctly use preferred pronouns. |
| Outcome: | The proposed framework evaluates language models' ability to correctly use preferred pronouns in English. |
Reasoning with Language Model Prompting: A Survey (2023.acl-long)
Copied to clipboard
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen
| Challenge: | Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications. |
| Approach: | They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners. |
| Outcome: | The proposed approaches have not been systematically reviewed and analyzed. |
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work in multimodal machine translation (MT) has shown that ambiguity can be resolved using accompanying context such as images. |
| Approach: | They propose a multimodal machine translation approach based on a strong text-only MT model and a novel guided self-attention mechanism to train it. |
| Outcome: | The proposed model outperforms existing models on EnglishFrench, EnglishGerman and EnglishCzech benchmarks and is freely available. |
Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Recent efforts to train cross-lingual models on source language fail to take advantage of data transfer . current methods focus on learning task-specific information from syntactical features or word-label relations in target language. |
| Approach: | They propose a hybrid knowledge-transfer approach that leverages a teacher-student framework . the model is evaluated on a distinct target language for which there is no labeled data . |
| Outcome: | The proposed model achieves state-of-the-art results on 9 morphologically-diverse target languages across 3 distinct datasets. |
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training (2023.acl-long)
Copied to clipboard
| Challenge: | Automated metrics are used for machine translation, but they are often considered to be black boxes with potential biases that are difficult to detect. |
| Approach: | They analyze automatic metrics from the perspective of their guidance for machine translation training. |
| Outcome: | The proposed measures improve the performance of machine translation models. |
Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | Despite recent progress towards scaling up multimodal vision-language models, these models struggle on compositional generalization benchmarks such as Winoground. |
| Approach: | They propose to use a cross-modal attention regularization loss to enforce relation alignment by capturing the semantic relation ‘in’ to match the visual attention from the mug to the grass. |
| Outcome: | The proposed approach improves Winoground Group score by 5.75 points . |
Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023.acl-long)
Copied to clipboard
| Challenge: | Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories. |
| Approach: | They propose a model that clusters dense persona descriptions into sparse categories and generates personalized responses from dialogue histories. |
| Outcome: | The proposed model improves on Chinese and English datasets. |
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for updating knowledge show little propagation of injected knowledge. |
| Approach: | They propose to inject individual facts into LMs and evaluate whether they can propagate injected facts while not changing predictions on other contexts. |
| Outcome: | The proposed model can make inferences based on injected facts and propagate them . existing methods show little propagation of injected knowledge . |
Explaining How Transformers Use Context to Build Predictions (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for analyzing input attributions for a model's prediction are unclear how prior words affect the model' s decision throughout the layers. |
| Approach: | They propose a procedure to analyze models for language generation using the Transformer and a comparison of their results with evidence of the linguistic phenomena. |
| Outcome: | The proposed method consistently aligns better than gradient-based and perturbation-based baselines and generates human-like source-target alignments for building predictions. |
DISCO: Distilling Counterfactuals with Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | high-quality counterfactual data is scarce for most tasks and not easily generated at scale. |
| Approach: | They propose a method for automatically generating high-quality counterfactual data at scale . they use a large general language model to generate phrasal perturbations and filter them . |
| Outcome: | The proposed method is task-agnostic and can be applied to the task of natural language inference. |
Non-Sequential Graph Script Induction via Multimedia Grounding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing scripts for everyday tasks are presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life. |
| Approach: | They propose to use loosely aligned videos to train a non-sequential graph script induction task by using a multimodal framework to ground procedural videos to WikiHow textual steps. |
| Outcome: | The proposed model outperforms the WikiHow linear baseline by 48.76% . it can predict future steps given a partial step sequence and generate explicit graph scripts . |
SCOTT: Self-Consistent Chain-of-Thought Distillation (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LMs) generate free-text rationales for their predictions via chain-of-thought prompting, but there is little guarantee that the generated rationale is consistent with LM’s predictions or faithfully justify the decisions. |
| Approach: | They propose a faithful knowledge distillation method to learn a small, self-consistent CoT model from a larger teacher model by contrastive decoding. |
| Outcome: | The proposed method yields comparable performance but is less faithful than baselines. |
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underutilized due to their difficult-to-understand contents and complex hierarchies. |
| Approach: | They propose to use clinical notes to learn more balanced knowledge from EHRs by assembling useful neutral words with rare keywords via note and taxonomy level hyperedges. |
| Outcome: | The proposed method can retain clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution. |
Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Contemporary leading-edge systems for abstractive (long) text summarization employ Transformer encoderdecoder architectures that only consider the nuclearity annotation . |
| Approach: | They propose to incorporate Rhetorical Structure Theory into a novel summarization model that incorporates both the types and uncertainty of rhetorical relations. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
Evaluating Open-Domain Question Answering in the Era of Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation models fail to identify lexical matching failures for open-domain question answering. |
| Approach: | They manually evaluate open-domain QA models by manually evaluating their answers on a popular benchmark. |
| Outcome: | The proposed model performs better on NQ-open than existing models and more than 50% of lexical matching failures are attributed to semantically equivalent answers. |
No clues good clues: out of context Lexical Relation Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PTLMs) are used to predict lexical relations between words. |
| Approach: | They propose to use pre-trained language models to fine-tune and exploit verbalized text for linguistically motivated tasks. |
| Outcome: | The proposed model outperforms graded Lexical Entailment and lexical relation classification with very simple prompts. |
Won’t Get Fooled Again: Answering Questions with False Premises (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) are often easily deceived by tricky questions such as “How many eyes does the sun have?” . |
| Approach: | They annotate a FalseQA dataset containing 2365 human-written FPQs and find that PLMs are capable of discriminating FPqs by fine-tuning on moderate numbers. |
| Outcome: | The proposed model can discriminate on FPQs by fine-tuning on moderate numbers of examples and generate reasonable explanations for false premise questions. |
What the DAAM: Interpreting Stable Diffusion Using Cross Attention (2023.acl-long)
Copied to clipboard
Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
| Challenge: | a new text-image attribution analysis model for text-to-image generation is understudied due to ethical constraints . corporators have restricted the general public from using the models and their weights . |
| Approach: | They perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. |
| Outcome: | The proposed method achieves a competitive 58.8-64.8 mIoU on noun segmentation and fair to good mean opinion scores on all parts of speech rated by humans . it also achieves good attribution quality on all part of speech, rated in humans - and the first to interpret large diffusion models from a visuolinguistic perspective. |
Zero-shot Faithful Factual Error Correction (2023.acl-long)
Copied to clipboard
| Challenge: | Using machines to correct factual errors is in high demand and requires a significant amount of human effort. |
| Approach: | They propose a zero-shot framework that asks questions about input claims and seeks correct answers from the given evidence to correct factual errors faithfully. |
| Outcome: | The proposed framework outperforms fully-supervised methods on the FEVER and SciFact datasets and is more faithful. |
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification (2023.acl-long)
Copied to clipboard
| Challenge: | Recent methods for event schema induction use information extraction systems to construct event graph instances from documents . compared to the previous state-of-the-art closed-domain schema inducing model, human assessors were able to cover 10% more events when translating the schemas into coherent stories . |
| Approach: | They propose to treat event schemas as commonsense knowledge that can be derived from large language models. |
| Outcome: | The proposed method simplifies the schema induction process and improves readability. |
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. |
| Approach: | They propose to use few-shot learning settings to fine-tune the sentiment classification model using manual or automatically generated prompts. |
| Outcome: | The proposed method outperforms the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task. |
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging (2023.acl-long)
Copied to clipboard
| Challenge: | Massively multilingual language models have shown strong performance in zero-shot (ZS-XLT) and few-shot cross-lingual transfer setups where models are fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). |
| Approach: | They propose a method that averages different checkpoints during task fine-tuning to improve model robustness. |
| Outcome: | The proposed method overestimates model performance in cross-lingual transfer setups where models are evaluated at checkpoints that generalize best to validation instances in the target languages. |
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%. |
| Approach: | They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives. |
| Outcome: | The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets. |
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that context-free grammars are not natural for modeling discontinuous language phenomena such as extrapositions and cross-serial dependencies. |
| Approach: | They propose a grammar induction approach with mildly context-sensitive grammars for unsupervised discontinuous parsing. |
| Outcome: | Experiments on German and Dutch show that the proposed grammar induction method is beneficial for unsupervised parsing. |
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Transformers struggle to model several formal languages when compared to recurrent models. |
| Approach: | They conduct an extensive empirical study on Boolean functions to demonstrate that Transformers are relatively more biased towards functions of low sensitivity . they also show that Transformer's generalize near perfectly even in the presence of noisy labels whereas recurrent models overfit and achieve poor generalization accuracy. |
| Outcome: | The results show that Transformers generalize near perfectly even in noisy Boolean functions whereas recurrent models overfit and achieve poor generalization accuracy. |
Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation (2023.acl-long)
Copied to clipboard
| Challenge: | a counterspeech with a certain intent may not be sufficient in every situation due to complex nature of hate speech . a novel framework for intent-conditioned counterseech generation is proposed to address the pervasive issue of hateful speech on the internet. |
| Approach: | They propose a framework for intent-conditioned counterspeech generation that leverages intent-specific representations and a fusion module to incorporate intent-related information into the model. |
| Outcome: | The proposed framework outperforms baselines by 10% across evaluation metrics. |
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation (2023.acl-long)
Copied to clipboard
Suraj Kothawade, Anmol Mekala, D.Chandra Sekhara Hetha Havya, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi
| Challenge: | State-of-the-art automatic speech recognition systems exhibit disparate performance on varying speech accents. |
| Approach: | They propose to use submodular mutual information to find the most informative set of utterances matching a target accent within a fixed budget. |
| Outcome: | The proposed model is 3-5 times more label-efficient on the Indic-TTS and L2 datasets than other methods. |
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have a number of shortcomings, including lack of factual correctness. |
| Approach: | They propose a framework to increase prediction factuality by post-editing reasoning chains . they propose to use large language models to generate interpretable reasoning chains. |
| Outcome: | The proposed framework leads to accuracy improvements in open-domain question-answering tasks. |
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)
Copied to clipboard
Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
| Challenge: | Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains. |
| Approach: | They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore. |
| Outcome: | The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore. |
Node Placement in Argument Maps: Modeling Unidirectional Relations in High & Low-Resource Scenarios (2023.acl-long)
Copied to clipboard
| Challenge: | Argument maps structure discourse into nodes with each node being an argument that supports or opposes its parent argument. |
| Approach: | They propose a task of node placement: suggesting candidate nodes as parents for a new contribution. |
| Outcome: | The proposed method improves the quality of the argument maps and reduces redundancy. |
Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained Multilingual Language Models have shown a strong ability to transfer knowledge across languages. |
| Approach: | They examine factors contributing to the ability of MLLMs to perform zero-shot cross-lingual transfer . they identify consensuses among studies with consistent findings and resolve conflicts . |
| Outcome: | The authors outline and discuss factors that contribute to the ability of MLLMs to perform zero-shot cross-lingual transfer. |
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have been used to evaluate language generation tasks . pretrained error analysis can be used to refine the generated sentence toward higher confidence . |
| Approach: | They propose to combine pretrained language model based metrics with human-like error analysis to improve sentence confidence. |
| Outcome: | The proposed method outperforms top-scoring metrics in 19/25 settings. |
Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for implicit discourse relation recognition (IDRR) lack connectives, which is a major challenge in discourse analysis research. |
| Approach: | They propose a method to predict latent correlations between connectives and discourse relations using a knowledge distillation approach. |
| Outcome: | The proposed method outperforms state-of-the-art models on coarse-grained and fine-grain discourse relations and can be transferred to explicit discourse relation recognition and achieve acceptable performance. |
What is the best recipe for character-level encoder-only modelling? (2023.acl-long)
Copied to clipboard
| Challenge: | aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. |
| Approach: | They aim to find the best way to build and train character-level BERT-like models by comparing architectural innovations with pretraining objectives. |
| Outcome: | The proposed model outperforms a token-based model on a set of evaluation tasks with a fixed training procedure. |
Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies address the problem of translating English data into other languages, but they are limited in form and scale. |
| Approach: | They propose a framework to unify cross-lingual and cross-modal pre-training by using English data. |
| Outcome: | The proposed framework unifies cross-lingual and cross-modal pre-training on different data. |
Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER (2023.acl-long)
Copied to clipboard
Ruotian Ma, Xuanting Chen, Zhang Lin, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen
| Challenge: | Existing Named Entity Recognition systems are typically trained on a large-scale dataset with predefined entity classes, then deployed for entity recognition on the test data without further adaptation or refinement. |
| Approach: | They propose a representation learning method that adaptively detects entity clusters in "O" and two effective distance-based relabeling strategies for better learning the old classes. |
| Outcome: | The proposed method achieves 10.62% improvement over the baseline methods. |
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination (2023.acl-long)
Copied to clipboard
| Challenge: | Current neural machine translation (NMT) relies on parallel sentences, which obstructs the development of NMT for minor languages. |
| Approach: | They propose an unsupervised multimodal machine translation setup where the model is trained with source-text image pairs and tested with only source- text inputs. |
| Outcome: | The proposed model outperforms the baseline model on the task and setup, helping yield translations with better completeness, relevance and fluency without relying on paired images. |
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) are limited by the cost of labeling and labeling, especially for low-resource languages. |
| Approach: | They propose a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other. |
| Outcome: | The proposed framework achieves superior results on benchmark datasets and can generalize to distant languages. |
Dialect-robust Evaluation of Generated Text (2023.acl-long)
Copied to clipboard
Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann
| Challenge: | Existing evaluation metrics that are not robust to dialect variation are difficult to measure for many groups of users and can penalize systems for producing text in lower-resource dialects. |
| Approach: | They propose a dialect-robust evaluation metric that produces the same score for system outputs that share the same semantics but are expressed in different dialects. |
| Outcome: | The proposed method significantly improves dialect robustness while preserving the correlation between automated metrics and human ratings. |
Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing terminology constraint test sets are blind to this issue due to oversimplified settings . PH methods retain high constraint accuracy but lower translation quality . |
| Approach: | They propose a method that replaces terminology terms with ordered labels . placeholder methods are better at retaining high constraint accuracy but lower translation quality . |
| Outcome: | The proposed method achieves high accuracy and translation quality regardless of the number or length of constraints. |
Language model acceptability judgements are not always robust to context (2023.acl-long)
Copied to clipboard
Koustuv Sinha, Jon Gauthier, Aaron Mueller, Kanishka Misra, Keren Fuentes, Roger Levy, Adina Williams
| Challenge: | a recent study found that models prefer acceptable inputs over acceptable ones. |
| Approach: | They find that model judgements are generally robust when placed in randomly sampled linguistic contexts, but unstable when contexts match the test stimuli in syntactic structure. |
| Outcome: | The proposed model performance improves when contexts match syntactic structure, and declines when they are unacceptable. |
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations (2023.acl-long)
Copied to clipboard
Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev
| Challenge: | Existing Table QA models are vulnerable to task-specific perturbations, such as replacing key question entities or shuffling table columns. |
| Approach: | They propose to use large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. |
| Outcome: | The proposed model significantly improves on existing Table QA models against human-annotated adversarial perturbations. |
Morphological Inflection: A Reality Check (2023.acl-long)
Copied to clipboard
| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
TOME: A Two-stage Approach for Model-based Retrieval (2023.acl-long)
Copied to clipboard
| Challenge: | Recent research has focused on model-based retrieval, which discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Approach: | They propose a model-based retrieval approach that discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Outcome: | The proposed approach eliminates the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
Using Neural Machine Translation for Generating Diverse Challenging Exercises for Language Learner (2023.acl-long)
Copied to clipboard
| Challenge: | a common challenge for language learners is understanding how to appropriately use words that may have similar meanings but are used in different contexts. |
| Approach: | They propose a method to automatically generate distractors for cloze exercises for English language learners using round-trip neural machine translation. |
| Outcome: | The proposed method generates distractors for cloze exercises for English learners . it shows that the generated distractors are of the same difficulty as human distractors . |
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks (2023.acl-long)
Copied to clipboard
| Challenge: | Arguments often do not make explicit how a conclusion follows from its premises . we present a method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that is efficient and high-quality . |
| Approach: | They propose an unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) they use triplet similarities to extract contextually relevant knowledge paths . |
| Outcome: | The proposed method outperforms baselines and a GPT-3 based system in a knowledge-intense argumentation task. |
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot sentence embeddings are not robust enough to measure sentence similarity due to the ambiguity and variability of linguistic expressions. |
| Approach: | They propose a mutual information-based contrastive learning framework that imposes alignment between different views during contrastive training. |
| Outcome: | The proposed framework shows strong performance in few-shot learning domain compared to state-of-the-art methods, but comparable in full-shot scenario. |
Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency (2023.acl-long)
Copied to clipboard
| Challenge: | Numeracy is the most prevalent form of non-linguistic information embedded in textual corpora. |
| Approach: | They propose a framework for non-linguistic skill injection for LLMs that incorporates information-theoretic interventions and skill-specific losses to enable the learning of strict arithmetic reasoning. |
| Outcome: | The proposed model outperforms the state-of-the-art on injected non-linguistic skills and on linguistic knowledge retention with a fraction of the non-language training data (1/4) and zero additional synthetic linguistic training data. |
Forgotten Knowledge: Examining the Citational Amnesia in NLP (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study examines how far back in time we tend to cite papers . citation patterns are correlated with age, age, and other factors . |
| Approach: | They analyze citation patterns across time and examine temporal changes . they find that 62% of cited papers are from the immediate five years prior to publication . |
| Outcome: | The authors show that citing papers is the primary method of scientific writing . they show that the trend has reversed and current papers have low temporal diversity . |
Measuring the Instability of Fine-Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Many previous studies have investigated fine-tuning pre-trained language models on downstream tasks with varying random seeds, but they only used the standard deviation of performance scores (SD) as their measure, which is a narrow characterization of instability. |
| Approach: | They propose a systematic evaluation framework for the standard deviation of performance scores (SD) and six other measures quantifying instability of different granularity levels. |
| Outcome: | The proposed framework will be used to evaluate the validity of these measures and to improve them. |
FairPrism: Evaluating Fairness-Related Harms in Text Generation (2023.acl-long)
Copied to clipboard
Eve Fleisig, Aubrie Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, Hanna Wallach
| Challenge: | FairPrism dataset provides a framework for measuring and mitigating fairness-related harms caused by AI text generation systems. |
| Approach: | They propose a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. |
| Outcome: | FairPrism is a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering harms relating to gender and sexuality. |
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
Copied to clipboard
Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
SIMMC-VR: A Task-oriented Multimodal Dialog Dataset with Situated and Immersive VR Streams (2023.acl-long)
Copied to clipboard
Te-Lin Wu, Satwik Kottur, Andrea Madotto, Mahmoud Azab, Pedro Rodriguez, Babak Damavandi, Nanyun Peng, Seungwhan Moon
| Challenge: | Existing models lack a large-scale benchmark to capture user–assistant interactions . et al., 2022: 145-160. |
| Approach: | They propose a video-grounded task-oriented dialog dataset that captures real-world AI-assisted user scenarios in VR. |
| Outcome: | The proposed dataset captures real-world AI-assisted user scenarios in VR. |
Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | a handful of studies have explored ICL in a cross-lingual setting . emergence of large-scale, pretrained, Transformer-based language models has marked the commencement of an avant-garde era in NLP. |
| Approach: | They propose a novel prompt construction strategy to bridge the gap between ICL and cross-lingual text classification. |
| Outcome: | The proposed approach outperforms random prompt selection by a large margin across three tasks using 44 different cross-lingual pairs. |
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning (2023.acl-long)
Copied to clipboard
Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren
| Challenge: | Existing methods to improve logical reasoning skills require complex data processing. |
| Approach: | They propose an adaptive pretraining approach to improve logical reasoning over text . they use a subset of Wikipedia sentences for pretraining and a sentence-level classification loss . |
| Outcome: | The proposed model outperforms baselines on LogiQA and ReClor. |
MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Recent tabular question answering models only answer questions over a single table . multi-table operations often result in tabular outputs . |
| Approach: | They propose a model that answers questions over multiple tables and generalizes to generate tabular answers. |
| Outcome: | The proposed model outperforms state-of-the-art single table QA models on a multi-table QA setting. |
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)
Copied to clipboard
Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
| Challenge: | Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy. |
| Approach: | They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction. |
| Outcome: | The proposed model improves generalization ability and makes distant link prediction significantly easier. |
CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Automated diagnosis (AD) is a critical application of AI in healthcare . despite its simplicity and superior performance, a decline in disease diagnosis accuracy is observed . |
| Approach: | They propose a new collaborative disease and symptom generation framework to improve automatic diagnosis. |
| Outcome: | The Transformer-based method achieves an average 2.3% improvement over previous state-of-the-art methods . it can be used to query patients about their symptoms and health concerns . |
Long-Tailed Question Answering in an Open World (2023.acl-long)
Copied to clipboard
| Challenge: | Existing QA approaches require access to seen tasks or do not explicitly model samples from unseen tasks. |
| Approach: | They propose an open-tailed QA model that encourages knowledge sharing between head, tail and unseen tasks and explicitly mines knowledge from a large pre-trained language model. |
| Outcome: | The proposed model outperforms the state-of-the-art on a large-scale dataset. |
Parallel Context Windows for Large Language Models (2023.acl-long)
Copied to clipboard
Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
| Challenge: | Existing efforts to address context window limitation for off-the-shelf LLMs involve training specialized architectures. |
| Approach: | They propose a method that carves a long context into chunks and restricts attention to apply only within each window. |
| Outcome: | The proposed method shows significant improvements on in-context learning tasks with diverse input and output spaces. |
Efficient Transformers with Dynamic Token Pooling (2023.acl-long)
Copied to clipboard
| Challenge: | Hourglass Transformers is a computationally efficient model that can be used to reduce the sequence length in the intermediate layers. |
| Approach: | They propose a dynamic-pooling mechanism which predicts segment boundaries in an autoregressive fashion. |
| Outcome: | The proposed model is faster and more accurate than vanilla Transformers and fixed-length pooling within the same computational budget. |
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) models achieve consistent performance gains in DocRE, but their underlying decision rules are still understudied. |
| Approach: | They propose to use annotations to provide rationales for document-level relation extraction (DocRE) they then propose to apply a method to evaluate models' reasoning capabilities . |
| Outcome: | The proposed models exhibit different reasoning processes in contrast to humans . the proposed models render models more trustworthy and robust to be deployed in real-world scenarios. |
ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)
Copied to clipboard
Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
| Challenge: | Existing studies show that causal language models lack expressiveness due to poor discrimination ability. |
| Approach: | They propose a contrastive learning framework that enhances discrimination of representations and bridges the gap with encoder-only models. |
| Outcome: | The proposed framework improves discrimination and source code generation capabilities on a variety of downstream tasks. |
Advancing Multi-Criteria Chinese Word Segmentation Through Criterion Classification and Denoising (2023.acl-long)
Copied to clipboard
| Challenge: | Recent research on multi-criteria Chinese word segmentation focuses on building complex private structures, adding more handcrafted features, or introducing complex optimization processes. |
| Approach: | They propose a model that fits multiple Chinese word segments using input-hint inputs. |
| Outcome: | The proposed model achieves state-of-the-art (SoTA) performance on multiple datasets simultaneously. |
Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments. |
| Approach: | They propose a prompt-based multi-level implicit discourse relation recognition framework that leverages parameter-efficient prompt tuning to drive inputted arguments to match the pre-trained space. |
| Outcome: | The proposed framework achieves comparable results on PDTB 2.0 and 3.0 using about 0.1% trainable parameters compared with baselines. |
Contrastive Learning with Adversarial Examples for Alleviating Pathology of Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing interpretation methods fail to obtain faithful attributions on these models, thereby failing to reveal potential flaws and biases. |
| Approach: | They propose a Contrastive learning regularization method which calibrates the sentence representation of out-of-distribution examples and utilizes adversarial examples to introduce direction information in regularization. |
| Outcome: | The proposed method alleviates the model pathology while impacting generalization ability on in-distribution examples and thus helps interpretation methods obtain more faithful results. |
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales (2023.acl-long)
Copied to clipboard
| Challenge: | Social biases and stereotypes are embedded in our culture through their presence in our stories. |
| Approach: | They propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. |
| Outcome: | The proposed framework extracts a story’s verb-based event chain for each of its characters as well as character attributes such as gender. |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
| Outcome: | The proposed model can be applied to various downstream dialogue tasks. |
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in automated reasoning with natural text suffer from a combinatorial explosion of the search space and high failure rates for problems requiring longer chains of reasoning. |
| Approach: | They propose a Backward Chaining algorithm that decomposes reasoning into four sub-modules and implements it by few-shot prompted LLM inference. |
| Outcome: | The proposed algorithm achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets. |
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives (2023.acl-long)
Copied to clipboard
Silin Gao, Beatriz Borges, Soyoung Oh, Deniz Bayazit, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut
| Challenge: | a new knowledge graph for personas based on human-validated persona facts is constructed to model diverse persona attributes . a variety of persona characteristics are required to sustain coherent narratives . |
| Approach: | They construct a large-scale persona commonsense knowledge graph with 100K human-validated persona facts. |
| Outcome: | The proposed graph contains rich and precise world persona inferences that help systems generate more consistent and engaging narratives. |
OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech. |
| Approach: | They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment . |
| Outcome: | The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively. |
Retrieval-free Knowledge Injection through Multi-Document Traversal for Dialogue Models (2023.acl-long)
Copied to clipboard
Rui Wang, Jianzhu Bao, Fei Mi, Yi Chen, Hongru Wang, Yasheng Wang, Yitong Li, Lifeng Shang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Existing research on retrieval-augmented and retrieval free dialogue models focuses on retrieving knowledge from external sources and rely on finely annotated retrieval training data and knowledge-grounded responses. |
| Approach: | They propose a retrieval-free approach by turning knowledge documents into simulated multi-turn dialogues using a Multi-Document Traversal algorithm. |
| Outcome: | The proposed approach outperforms retrieval-augmented models while being cheaper and faster at domain transfer. |
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval (2023.acl-long)
Copied to clipboard
| Challenge: | Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets. |
| Approach: | They propose a method to capture matching signal to improve generalization of dense retrieval by capturing matching signal between two texts. |
| Outcome: | The proposed method can be combined with different training methods to improve generalization ability without additional inference overhead and target domain data. |
Multiview Identifiers Enhanced Generative Retrieval (2023.acl-long)
Copied to clipboard
| Challenge: | Current approaches use a numeric ID or text piece as the identifier, but these identifieres cannot cover a passage’s content well. |
| Approach: | They propose a new type of identifier that is generated based on the content of a passage and could integrate contextualized information that text pieces lack. |
| Outcome: | The proposed approach performs the best in generative retrieval on three public datasets. |
Prompting Language Models for Linguistic Structure (2023.acl-long)
Copied to clipboard
| Challenge: | Existing prompting methods can test this hypothesis on autoregressive PLMs. |
| Approach: | They propose a structured prompting approach for linguistic structured prediction tasks that performs zero- and few-shot sequence tagging with autoregressive PLMs. |
| Outcome: | The proposed approach shows that the model can perform few-shot sequence tagging on part-of-speech taging, named entity recognition, and sentence chunking tasks. |
Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | a study of FOMC pronouncements shows how important the FOMC communications are . hawkish-dovish classification is difficult because of the negative connotations of words . |
| Approach: | They propose to use a dataset to classify FOMC monetary policy stances . they construct a measure of monetary stance for the FOMC document release days . |
| Outcome: | The proposed model is based on a best-performing model and is available on Huggingface and GitHub under CC BY-NC 4.0 license. |
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2023.acl-long)
Copied to clipboard
Jun Zhao, WenYu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, Mingming Sun
| Challenge: | Existing methods for zero-shot relation extraction lack explicit modeling of matching pattern . et al. (2018) show that our method achieves higher matching accuracy and faster inference speed . |
| Approach: | They propose a fine-grained semantic matching method tailored for zero-shot relation extraction . they decompose sentence-level similarity score into entity matching score and context matching score . |
| Outcome: | The proposed method achieves higher matching accuracy and faster inference speed than state-of-the-art methods. |
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration (2023.acl-long)
Copied to clipboard
Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, Byoungpil Kim, Gunhee Kim, Eun-Ju Lee, Yong Lim, Alice Oh, Sangchul Park, Jung-Woo Ha
| Challenge: | Existing studies focus on coping with social harms that large language models pose . however, discussions on sensitive issues can become toxic even if the users are well-intentioned. |
| Approach: | They propose to use Korean dataset to test whether LLMs can generate offensive content and propagate prejudices. |
| Outcome: | The proposed dataset shows that acceptable response generation improves for HyperCLOVA and GPT-3. |
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation (2023.acl-long)
Copied to clipboard
| Challenge: | Despite the growing number of Korean learners, little research has been conducted on Korean grammatical error correction (GEC) despite the difficulties of the Korean language, there is no evaluation benchmark for Korean GEC. |
| Approach: | They propose to use Korean grammar error correction datasets to train a machine learning model that can automatically annotate Korean errors from parallel corpora. |
| Outcome: | The proposed model outperforms the currently used statistical Korean GEC system on a wider range of error types. |
FLamE: Few-shot Learning from Natural Language Explanations (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has shown limited utility of natural language explanations in improving classification. |
| Approach: | They propose a two-stage few-shot learning framework that generates explanations and fine-tunes a smaller model with generated explanations. |
| Outcome: | The proposed framework increases inference accuracy over strong baselines, but human evaluation reveals that the majority of generated explanations does not adequately justify classification decisions. |
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning (2023.acl-long)
Copied to clipboard
Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray
| Challenge: | Existing text-based reinforcement learning agents use embeddings as representations for observation and are fed to an action scorer for predicting the next action. |
| Approach: | They propose a novel neurosymbolic agent that combines a semantic parser and a rule induction system to learn interpretable rules as policies. |
| Outcome: | The proposed method outperforms deep learning-based methods on established text-based game benchmarks on unobserved games and on unseen games. |
Counterfactual Debiasing for Fact Verification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for debiasing factchecking models learn such biases instead of understanding the semantic relationship between the claim and evidence. |
| Approach: | They propose a counterfactual framework CLEVER which is augmentation-free and mitigates biases on the inference stage. |
| Outcome: | The proposed method is augmentation-free and mitigates biases on the inference stage. |
What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics (2023.acl-long)
Copied to clipboard
| Challenge: | Much research has focused on evaluating whether large language models encode stereotypical/harmful associations. |
| Approach: | They propose to use two datasets from human experiments to examine how word preferences in a large language model reflect social attitudes about gender. |
| Outcome: | The language model BERT takes into account factors that shape human lexical choice of such language, but may not weigh those factors in the same way people do. |
Rethinking Multimodal Entity and Relation Extraction from a Translation Point of View (2023.acl-long)
Copied to clipboard
| Challenge: | Special attention is paid to the cross-modal misalignment in text-image datasets which may mislead the learning. |
| Approach: | They propose a multimodal back-translation method which uses diffusion-based generative models for pseudo-paralleled pairs and a divergence estimator to construct a high-resource corpora as a bridge for low-ressource learners. |
| Outcome: | The proposed method outperforms 14 state-of-the-art methods in both entity and relation extraction tasks. |
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on factual inconsistency in abstractive summarization addresses this problem. |
| Approach: | They propose a dataset with fine-grained factual error annotations named DIASUMFACT and an unsupervised model named ENDERANKER. |
| Outcome: | The proposed model performs on par with the state-of-the-art models while requiring fewer resources. |
Improving the Robustness of Summarization Systems with Dual Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. |
| Approach: | They propose a SummAttacker approach to generate adversarial samples based on pre-trained language models that can generate word-level synonym substitution and noise. |
| Outcome: | The proposed model performs better on noisy, attacked, and clean datasets than baseline models and is more robust on noisy and attacked datasets. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
| Approach: | They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input. |
| Outcome: | The proposed method can perform human-in-the-loop error correction tasks. |
Deep Model Compression Also Helps Models Capture Ambiguity (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that our method reduces the model size significantly and improves latency. |
| Approach: | They propose a method to capture the degree of relationship between a sample and its candidate classes by deep model compression. |
| Outcome: | The proposed method reduces model size significantly and improves latency. |
Are Experts Needed? On Human Evaluation of Counselling Reflection Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Language models have been used to generate reflections automatically, but human evaluation is challenging due to the cost of hiring experts. |
| Approach: | They ask laypeople and experts to annotate synthetic reflections and human reflections from actual therapists. |
| Outcome: | The proposed method shows that laypeople and experts are reliable annotators and have moderate-to-strong inter-group correlation. |
PairSpanBERT: An Enhanced Language Model for Bridging Resolution (2023.acl-long)
Copied to clipboard
| Challenge: | bridging resolution is crucial for machine comprehension of discourse entities for various downstream applications. |
| Approach: | They propose a SpanBERT-based pre-trained model specialized for bridging resolution. |
| Outcome: | The proposed model achieves the best results on three evaluation datasets for bridging resolution despite the noise inherent in the automatically generated data . |
Compounding Geometric Operations for Knowledge Graph Completion (2023.acl-long)
Copied to clipboard
| Challenge: | Knowledge graph embedding (KGE) is one of the most fundamental problems in AI research. |
| Approach: | They propose a new knowledge graph embedding model by leveraging translation, rotation, and scaling operations to form a composite one. |
| Outcome: | The proposed model outperforms existing models on three KG prediction tasks. |
Few-shot In-context Learning on Knowledge Base Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | KB-BINDER enables few-shot in-context learning over knowledge base questions . KBQA is a difficult problem due to the heterogeneity of knowledge bases . |
| Approach: | They propose a framework that enables few-shot in-context learning over KBQA tasks. |
| Outcome: | The proposed framework can outperform state-of-the-art models on GraphQA and MetaQA datasets. |
Fact-Checking Complex Claims with Program-Guided Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Fact-checking real-world claims often requires collecting multiple pieces of evidence and complex multi-step reasoning. |
| Approach: | They propose a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. |
| Outcome: | The proposed model outperforms seven baselines on two fact-checking datasets and has explicit output programs that benefit human debugging. |
Patton: Language Model Pretraining on Text-Rich Networks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for text-rich networks do not take inter-document structure into account. |
| Approach: | They propose a pretraining framework for a text-rich network using a masked language model and a masking node prediction framework. |
| Outcome: | The proposed model outperforms baselines on four tasks in academic and e-commerce domains. |
Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size. |
| Approach: | They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally. |
| Outcome: | The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval. |
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Existing curriculum learning approaches often employ a single criterion of difficulty in their training paradigms. |
| Approach: | They propose a new approach that builds on graph complexity formalisms and model competence during training. |
| Outcome: | The proposed approach improves learning efficiency on real-world link prediction and node classification tasks. |
When and how to paraphrase for named entity recognition? (2023.acl-long)
Copied to clipboard
Saket Sharma, Aviral Joshi, Yiyun Zhao, Namrata Mukhija, Hanoz Bhathena, Prateek Singh, Sashank Santhanam
| Challenge: | Named entity recognition (NER) is a key component underpinning many industrial pipelines for a variety of downstream applications. |
| Approach: | They propose to use back translation to annotate entity spans in generations and propose a paraphraser with a larger dataset. |
| Outcome: | The proposed method improves NER performance across different datasets with gold annotations and paraphrasing strength. |
UniEvent: Unified Generative Model with Multi-Dimensional Prefix for Zero-Shot Event-Relational Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Reasoning about events and their relations is an indispensable ability to fulfill various event-centric or common-sense reasoning tasks. |
| Approach: | They propose a multi-task learning framework that organizes event relational reasoning tasks into a coordinate system with multiple axes, representing inter-event relations and reasoning formulations. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on zero-shot and supervised reasoning tasks. |
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-text Rationales (2023.acl-long)
Copied to clipboard
Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi, Xiang Ren
| Challenge: | Existing metrics like task performance of the LM generating the rationales or similarity between generated and gold rationale are not good indicators of their human utility. |
| Approach: | They propose to use a large language model to generate rationales with better human utility by estimating its conciseness and novelty. |
| Outcome: | The proposed model can measure human utility to a better extent by estimating its usefulness in answering similar unseen instances. |
Automatic Annotation of Direct Speech in Written French Narratives (2023.acl-long)
Copied to clipboard
| Challenge: | a new framework for AADS annotation in written text is needed for literary studies. |
| Approach: | They propose to use automatic annotation of direct speech (AADS) in written text to compare works by different authors . they adapted a large-to-date French narrative dataset annotated with DS per word . |
| Outcome: | The proposed framework is a step further to encourage more research on the topic. |
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations (2023.acl-long)
Copied to clipboard
| Challenge: | Named entity recognition models rely on domain-specific dictionaries provided by experts . however, such dictionary sets are infeasible in many domains where they do not exist . |
| Approach: | They propose a framework that generates NER datasets with high-coverage pseudo-dictionaries . phrase retrieval models are used to retrieve popular entities rather than rare ones . |
| Outcome: | The proposed framework outperforms the previous best model by an average F1 score of 4.7 across five NER benchmark datasets. |
Dynamic Transformers Provide a False Sense of Efficiency (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models typically lead to high computational cost during inference. |
| Approach: | They propose a slowdown attack framework that can reduce inference efficiency by 80% by leveraging existing adversarial attacks targeting model accuracy. |
| Outcome: | The proposed framework can reduce the efficiency of multi-exit models by 80% on average, validating its effectiveness and generalization ability. |
Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets do not allow for a fine-grained cross-lingual evaluation and mainly permit comparisons on a language level. |
| Approach: | They propose a morphologically-aware framework for behavioral testing of NLP models that generates tests in light of specific linguistic features in 12 typologically diverse languages. |
| Outcome: | The proposed framework evaluates state-of-the-art language models on the generated tests. |
Local Byte Fusion for Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing NLP models rely on a pre-built subword tokenizer to tokenize a sentence . this can be rigid and subwords from low-resource languages are under-represented . |
| Approach: | They propose a method for byte-based machine translation that aggregates local semantic information. |
| Outcome: | The proposed method improves on multilingual translation and cross-lingual transfer . it is parameter-efficient and performs competitively to subword models, it is shown . |
Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Prior sentence segmentation tools rely on punctuation or require a large amount of training data . a new method for multilingual sentence segmenting is proposed to replace the best prior tools by using only sentence-segmented examples. |
| Approach: | They propose a punctuation-agnostic sentence segmentation method that uses newline characters which implicitly perform segmentation into paragraphs. |
| Outcome: | The proposed method outperforms all prior best sentence segmentation tools by 6.1% F1 points. |
Multi-target Backdoor Attacks for Code Pre-trained Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work for backdoor attacks on neural code models insert triggers into task-specific data for code-related downstream tasks, limiting the scope of attacks. |
| Approach: | They propose task-agnostic backdoor attacks for code pre-trained models . they use two learning strategies to implant backdoors into code understanding and generation models - Poisoned Seq2Seq learning and token representation learning . |
| Outcome: | The proposed model is pre-trained with two learning strategies to support the multi-target attack of downstream code understanding and generation tasks. |
Learning Better Masking for Better Language Model Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Existing PrLMs adopt a Random-Token Masking strategy with a fixed masking ratio and different contents are masked by an equal probability throughout the training. |
| Approach: | They propose two scheduled masking approaches that adaptively tune masking ratio and masked content in different training stages, which improves pre-training efficiency and effectiveness. |
| Outcome: | The proposed methods improve the pre-training efficiency and effectiveness on the downstream tasks. |
VisText: A Benchmark for Semantically Rich Chart Captioning (2023.acl-long)
Copied to clipboard
| Challenge: | Current approaches for automatically generating chart captions struggle to articulate the perceptual or cognitive features that are the hallmark of charts (e.g., complex trends and patterns). |
| Approach: | They propose a dataset of 12,441 pairs of charts and captions that describe charts’ construction, report key statistics, and identify perceptual and cognitive phenomena. |
| Outcome: | The proposed model generates coherent, semantically rich captions and performs on par with state-of-the-art chart captioning models across machine translation and text generation metrics. |
Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora (2023.acl-long)
Copied to clipboard
Svanhvít Lilja Ingólfsdóttir, Petur Ragnarsson, Haukur Jónsson, Haukur Simonarson, Vilhjalmur Thorsteinsson, Vésteinn Snæbjarnarson
| Challenge: | Spelling mistakes due to typos and rushed writing, nonstandard punctuation and spelling, and grammatical and stylistic issues are common to almost everyone who writes any kind of text. |
| Approach: | They propose to use a common subword unit vocabulary and byte-level encoding to fine tune two subword-level models and one byte level model on hand-corrected error corpora. |
| Outcome: | The proposed model improves accuracy for spelling and grammatical errors and more complex errors. |
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text (2023.acl-long)
Copied to clipboard
| Challenge: | Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-difference (ID) examples. |
| Approach: | They propose a method that integrates strengths and weaknesses of both methods . they use a fine-tuned model as the teacher to teach a randomly initialized student model . |
| Outcome: | The proposed method outperforms human evaluators in the pair-expert task on the Human ChatGPT Comparison Corpus. |
Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations. |
| Approach: | They propose to use an explainable complaint dataset to generate a commonsense-aware generative framework that can predict the complaint cause, severity level, emotion, and polarity of the text. |
| Outcome: | The proposed model can predict the complaint cause, severity level, emotion, and polarity of the text in addition to detecting whether it is a complaint or not. |
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2023.acl-long)
Copied to clipboard
| Challenge: | MMDialog is a dataset of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. |
| Approach: | They propose to use a curated set of 1.08 million dialogues with 1.53 million unique images to generalize the open domain. |
| Outcome: | The proposed system can predict responses to multi-modal content with state-of-the-art techniques and measure their performance. |
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | End-to-end models learn to complete a task by directly learning all steps, without intermediary algorithms such as hand-crafted rules or post-processing. |
| Approach: | They propose to train end-to-end poetry generation conditioned on styles such as rhyme, meter, and alliteration . they pre-train ByGPT5, a new token-free decoder-only language model, and fine-tune it on a custom corpus of English and German quatrains . |
| Outcome: | The proposed model outperforms other models on a large custom corpus of English and German quatrains while being more parameter efficient and performing favorably compared to humans. |
Envisioning Future from the Past: Hierarchical Duality Learning for Multi-Turn Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to generate high quality responses rely on future text . |
| Approach: | They propose a hierarchical duality learning for dialogue to simulate human cognitive ability . they utilize hierarchically dualities at token hierarchy and utterance hierarchy to simulate duality . |
| Outcome: | The proposed model can generate high quality responses that connect both previous and follow-up dialogues. |
DualGATs: Dual Graph Attention Networks for Emotion Recognition in Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on speaker-aware context modeling, overlooking the discourse structure of the conversation. |
| Approach: | They propose Dual Graph ATtention networks to capture contextual dependencies in conversational contexts and integrate it into a speaker-aware GAT module. |
| Outcome: | The proposed model outperforms state-of-the-art models on four datasets and is highly efficient. |
Consistent Prototype Learning for Few-Shot Continual Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot continual relation extraction are overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle confusion of similar classes. |
| Approach: | They propose a few-shot continual relation extraction task that uses memory-enhanced modules to train a model on incrementally few-shot data to avoid forgetting old relations. |
| Outcome: | The proposed method outperforms existing methods on two commonly-used datasets. |
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | generative large language models (LLMs) are widely used but fine-tuned to improve performance on downstream applications leads to violations of model licenses, model theft, and copyright infringement. |
| Approach: | They propose to trace back the origin of a model trained to its pre-trained base model . they use different knowledge levels and attribution strategies to find out how the model was trained . |
| Outcome: | The proposed method can trace back 8 out of 10 fine tuned models with different knowledge levels and attribution strategies. |
Large Language Models Meet NL2Code: A Survey (2023.acl-long)
Copied to clipboard
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou
| Challenge: | generating code from a natural language description is a pressing and significant challenge in code intelligence. |
| Approach: | They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks. |
| Outcome: | The proposed model is compared with existing models on the HumanEval benchmark. |
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)
Copied to clipboard
| Challenge: | Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them. |
| Approach: | They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets. |
| Outcome: | The proposed architecture outperforms single-task learning (STL) and is expected to outperformed it. |
Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)
Copied to clipboard
| Challenge: | To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors. |
| Approach: | They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections. |
| Outcome: | The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule. |
Linguistic representations for fewer-shot relation extraction across domains (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on performance in several NLP tasks. |
| Approach: | They extend previous work to examine whether linguistic representations enhance generalizability . they incorporate syntactic and semantic graphs from off-the-shelf tools into a transformer-based architecture . |
| Outcome: | The proposed approach enhances generalization by providing cross-domain pivots . it also shows that syntactic and semantic graphs exhibit roughly equivalent utility . |
DarkBERT: A Language Model for the Dark Side of the Internet (2023.acl-long)
Copied to clipboard
| Challenge: | Recent research suggests that there are clear differences in the language used in the Dark Web compared to that of the Surface Web. |
| Approach: | They propose a language model that is pretrained on Dark Web data to combat extreme lexical diversity. |
| Outcome: | The proposed model outperforms existing models and may be useful for future research on the Dark Web. |
MDACE: MIMIC Documents Annotated with Code Evidence (2023.acl-long)
Copied to clipboard
Hua Cheng, Rana Jafari, April Russell, Russell Klopfer, Edmond Lu, Benjamin Striner, Matthew Gormley
| Challenge: | Computer-Assisted Coding (CAC) systems are required to provide supporting textual evidence to justify billing codes. |
| Approach: | They propose a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. |
| Outcome: | The proposed dataset can be used to evaluate evidence extraction methods for CAC systems, as well as the accuracy and interpretability of deep learning models for multi-label classification. |
Towards Zero-Shot Multilingual Transfer for Code-Switched Responses (2023.acl-long)
Copied to clipboard
| Challenge: | Recent task-oriented dialog systems have had great success building English-based personal assistants, but extending these systems to a global audience may take tremendous efforts. |
| Approach: | They propose a framework that allows for efficient transfer by learning task-specific representations and encapsulating source and target language representations. |
| Outcome: | The proposed framework is able to successfully transfer language knowledge even when the target language corpus is limited. |
One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Parameter-efficient transfer learning methods can be expensive in storage when applied to broader ranges of tasks. |
| Approach: | They propose a method that enables efficient sharing of a single PETL network across layers and tasks. |
| Outcome: | The proposed method outperforms other methods with 10% parameters required by the latter on various downstream tasks. |
Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models have been widely used in NLP, but their social or cultural impact is under-explored. |
| Approach: | They build a dataset consisting of numerous **C**hinese **C*omical **C***rosstalk scripts, which is for a popular Chinese performing art called ‘Xiangsheng’ or ‘’ since 1800s. |
| Outcome: | The proposed approach can generate humor as humans do, but it is still in its infancy. |
Convergence and Diversity in the Control Hierarchy (2023.acl-long)
Copied to clipboard
| Challenge: | Weir has defined a hierarchy of language classes whose second member (L2) is generated by tree-adjoining grammars (TAG), linear indexed grammars, combinatory categorial grammars and head grammars. |
| Approach: | They propose to extend Weir's mechanism of control to give a definition of controllable pushdown automata (PDAs) they propose to use a stricter notion of equivalence to allow for finer-grained comparisons than weak equvalence. |
| Outcome: | The proposed language classes are d-weakly equivalent to Weir's original two-level grammar, but not d strongly equivalent. |
ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Multimodal sentiment analysis aims to predict the sentiment of video content. |
| Approach: | They propose a framework that performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. |
| Outcome: | The proposed framework outperforms baseline methods on CH-SIMS, MOSI and MOSEI datasets on a range of metrics. |
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (2023.acl-long)
Copied to clipboard
Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
| Challenge: | Existing DSI approaches infer latent dialog structure without access to domain knowledge. |
| Approach: | They propose a neural-symbolic approach that injects symbolic knowledge into latent space of a generative neural model. |
| Outcome: | The proposed approach boosts performance over the canonical baselines over three dialog structure induction datasets. |
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)
Copied to clipboard
Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie
| Challenge: | Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation. |
| Approach: | They propose an Embedding Watermark method that implants backdoors on embeddings to protect copyright of large language models. |
| Outcome: | The proposed method protects the copyright of large language models without compromising service quality while minimizing the adverse impact on the original embeddings’ utility. |
Answering Ambiguous Questions via Iterative Prompting (2023.acl-long)
Copied to clipboard
| Challenge: | Empirical studies show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. |
| Approach: | They propose an answering model with a prompting model to address imperfections in open-domain question answering . Empirical studies show AmbigPrompt achieves state-of-the-art or competitive results . |
| Outcome: | The proposed framework improves on two commonly-used open benchmarks and achieves state-of-the-art or competitive results while using less memory and having a lower inference latency. |
A Dataset of Argumentative Dialogues on Scientific Papers (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in question-answering models have made them a great asset in accessing the content of scientific papers. |
| Approach: | They propose to use a dataset of 41 argumentative dialogues between scientists on 20 NLP papers to improve and evaluate their question-answering models. |
| Outcome: | The proposed dataset includes both exploratory and argumentative questions and answers in a dialogue discourse on a scientific paper. |
Massively Multilingual Lexical Specialization of Multilingual Transformers (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work focused on lexical specialization of monolingual PLMs with immense quantities of monolinguistic constraints, but recent work shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexicals. |
| Approach: | They propose to expose massively multilingual transformers to multilingual lexical knowledge at scale using BabelNet as a source of multilingual and cross-lingual type-level lexicon knowledge. |
| Outcome: | The proposed method shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexical tasks. |
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs (2023.acl-long)
Copied to clipboard
| Challenge: | Despite their success, even the largest language models make mistakes. |
| Approach: | They propose a framework where one language model can generate critiques to improve its peer's performance. |
| Outcome: | The proposed framework improves the performance of a fixed model 200 times its size by 10% over other models. |
WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
Copied to clipboard
| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
| Approach: | They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages. |
| Outcome: | The proposed model trains on 1.6M sentences from the English Common Crawl corpus and includes negative examples to better reflect the data on the web. |
NormBank: A Knowledge Bank of Situational Social Norms (2023.acl-long)
Copied to clipboard
| Challenge: | NormBank is a knowledge bank of 155k situational norms that can be used to ground flexible normative reasoning for interactive, assistive, and collaborative AI systems. |
| Approach: | They propose a new scheme for hierarchically organizing the seemingly unbounded social norms within a multivalent sociocultural frame. |
| Outcome: | The proposed framework can be used to ground flexible reasoning for interactive, assistive, and collaborative AI systems. |
DIP: Dead code Insertion based Black-box Attack for Programming Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to attack natural language models are difficult to apply due to the requirements. |
| Approach: | They propose a black-box attack method that generates adversarial examples using dead code insertion. |
| Outcome: | The proposed method outperforms the state-of-the-art black-box attack in both attack efficiency and attack quality on 9 victim downstream-task large code models. |
Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods focus on a single document’s coherence patterns, ignoring the underlying correlation between documents. |
| Approach: | They propose a GCN-based coherence model that captures structural similarities between documents by mining subgraph patterns and a heterogeneous graph for the training corpus. |
| Outcome: | The proposed model outperforms baseline models on discourse coherence and automated essay scoring tasks. |
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. |
| Approach: | They propose a hierarchy-aware tree isomorphism network to enhance the text representations with only syntactic information of the label hierarchy. |
| Outcome: | The proposed model could boost the performance of hierarchical text classification without prior statistics or label semantics without prior data. |
Contextual Knowledge Learning for Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. |
| Approach: | They propose a method to incorporate conversational context and knowledge into dialogue generation models . they use Latent Vectors to capture the relationship between context and knowing . |
| Outcome: | The proposed approach improves performance with two standard datasets and human evaluations. |
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | In order to improve translation efficiency, human translators perform post-editing on machine translations to correct errors. |
| Approach: | They propose to use the parameterized objective function of neural machine translation to deal with the TS problem without additional training. |
| Outcome: | The proposed method improves translation quality by 10.6 BLEU and reduces time overhead by 63.4% on benchmark datasets. |
Discourse-Centric Evaluation of Document-level Machine Translation with a New Densely Annotated Parallel Corpus of Novels (2023.acl-long)
Copied to clipboard
| Challenge: | Several recent papers claim to have achieved human parity at sentence-level machine translation. |
| Approach: | They propose to use a dataset with rich discourse annotations to evaluate MT performance . they find that MT outputs differ fundamentally from human translations in terms of latent discourse structures. |
| Outcome: | The proposed dataset builds upon the large-scale parallel corpus BWB . it covers 15,095 entity mentions in both languages and compares them to human translations . |
CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to translate speech signals into text are limited by the modality gap between speech and text. |
| Approach: | They propose Cross-modal Mixup via Optimal Transport to overcome the modality gap between speech and text by finding alignment between modalities. |
| Outcome: | Experiments on the MuST-C ST benchmark show that CMOT achieves an average BLEU of 30.0 in 8 translation directions, outperforming previous methods. |
On the Evaluation of Neural Selective Prediction Methods for Natural Language Processing (2023.acl-long)
Copied to clipboard
| Challenge: | Existing techniques for selective classification are lacking in the literature. |
| Approach: | They propose a methodological blueprint and a metric for calibrating confidence functions for selective prediction. |
| Outcome: | The proposed method improves on the GLUE benchmark and the proposed refinement metric provides a calibrated evaluation of confidence functions for selective prediction. |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
Copied to clipboard
Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
Text Style Transfer with Contrastive Transfer Pattern Mining (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text style transfer only focus on the transformation between styles, yet they do not take into account that this transformation can be achieved via different hidden transfer patterns. |
| Approach: | They propose a novel approach which automatically mines hidden transfer patterns to improve TST . they use a clustering module to automatically discover hidden transfer pattern from the data . |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance other methods to further improve their performance. |
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for event detection often fail to detect unseen or rare events due to the lack of domain knowledge. |
| Approach: | They propose a meta learning-based framework for zero-shot event detection that uses a prompt-based prompt and a trigger-aware soft verbalizer to efficiently project output to unseen tasks. |
| Outcome: | The proposed framework performs state-of-the-art in zero-shot and few-shot scenarios on benchmark datasets FewEvent and MAVEN. |
Text Style Transfer Back-Translation (2023.acl-long)
Copied to clipboard
Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing VSD work focuses on skewed spatial understanding of target objects . Existing work merely models the 2D geometrical vision features . |
| Approach: | They propose to incorporate 3D scene features into visual spatial description tasks by sampling topologically-diverse subgraphs from Go3D-S2G. |
| Outcome: | The proposed framework outperforms baselines on two VSD datasets and produces more spatially-diversified generation. |
Continual Knowledge Distillation for Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Current parallel corpora are not publicly accessible but trained models are more readily available. |
| Approach: | They propose a method to take advantage of existing translation models to improve one model of interest. |
| Outcome: | The proposed method improves on Chinese-English and German-English datasets and is robust to malicious models. |
Query Refinement Prompts for Closed-Book Long-Form QA (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) can answer questions and produce long-form texts, but the latter is difficult to evaluate since they are subjective in nature. |
| Approach: | They propose query refinement prompts that encourage LLMs to express multifacetedness and generate long-form answers covering multiple facets of the question. |
| Outcome: | The proposed model outperforms fully finetuned models in the closed-book setting and retrieve-then-generate open-book models. |
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding (2023.acl-long)
Copied to clipboard
Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, W.k. Chan, Chong-Wah Ngo, Mike Zheng Shou, Nan Duan
| Challenge: | Existing work on video temporal grounding for long videos is limited by existing datasets. |
| Approach: | They propose a query-guided window selection strategy and a coarse-to-fine mechanism to speed up inference for long videos. |
| Outcome: | The proposed framework accelerates inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results. |
Few-Shot Document-Level Event Argument Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. |
| Approach: | They propose a Few-Shot Document-Level Event Argument Extraction benchmark to capture event arguments that actually spread across sentences in documents. |
| Outcome: | The proposed task is very challenging with low performance and limited learning process . argument extraction depends on context from multiple sentences and learning process limited to very few examples . |
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
| Approach: | They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation. |
| Outcome: | The proposed dataset is syntactically more diverse than existing datasets while maintaining good semantic similarity. |
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge distillation techniques for neural machine translation lack special treatment on the top-1 information, which is limiting the potential of KD. |
| Approach: | They propose a method to distill knowledge from top-1 predictions of teachers and a technique to infuse more additional knowledge by distilling on the data without ground-truth targets. |
| Outcome: | The proposed method outperforms the vanilla word-level KD and outperfies the existing methods on three different students with different capacity gaps. |
Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering (2023.acl-long)
Copied to clipboard
Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan
| Challenge: | Existing question answering systems for tables and linked text are relatively unexplored. |
| Approach: | They propose a transformer-based question answering system that copes with distant supervision along both axes of the question and answer. |
| Outcome: | The proposed system beats baselines for HybridQA and OTT-QA with best EM and F1 scores on a held out test set. |
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level (2023.acl-long)
Copied to clipboard
Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin
| Challenge: | Existing research on HKGs rarely models the graphical and sequential structure of HKG, limiting their representation. |
| Approach: | They propose a Hierarchical Attention model for HKG Embedding that includes global-level and local-level attention to model the graphical structure of HKGs. |
| Outcome: | The proposed model achieves state-of-the-art performance on HKG standard datasets and addresses the issue of HKG multi-position prediction for the first time. |
ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate radiology reports only rely on high-level plans, but they lack important information. |
| Approach: | They propose an Observation-guided radiology Report Generation framework which generates free-text descriptions for a set of radiographs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods regarding text quality and clinical efficacy. |
Data Curation Alone Can Stabilize In-context Learning (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a new paradigm for few-shot learning with pretrainable large language models . however, randomly sampling examples from a training set leads to high variance in performance . |
| Approach: | They propose two methods to select training examples from a training set and then carefully curate them from corresponding subsets. |
| Outcome: | The proposed method improves accuracy over sampling from the entire training set. |
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)
Copied to clipboard
| Challenge: | Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge. |
| Approach: | They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed . |
| Outcome: | The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines . |
FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Large pre-trained models are capable of few-shot in-context learning (ICL) however, concatenated demonstrations are often excessively long and require additional computation. |
| Approach: | They propose to apply fusion-in-decoder (FiD) models to perform few-shot in-context learning (ICL) they propose to use concatenation-based, early-fusion, intermediate- and late-fusion methods to improve efficiency . |
| Outcome: | The proposed methods outperform concatenation-based models on 11 held-out tasks. |
S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction suffer from the inadequacy of large-scale annotated data. |
| Approach: | They propose a framework for two-stage self-training with synthetic data for relation extraction . |
| Outcome: | The proposed framework is based on two-stage self-training with synthetic data . it is able to synthesize large quantities of training data and iteratively and alternately learn from synthetic and golden data together. |
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Conventional fine-tuning works through updating all of the parameters in the pre-trained model, but as the size of pre-train models grows, it can be time-consuming and computationally expensive. |
| Approach: | They propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. |
| Outcome: | The proposed framework saves 25% inference FLOPs while maintaining competitive downstream performance. |
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing empathetic dialogue models only consider the affective aspect of empathy, which limits the capability of emotional response generation. |
| Approach: | They propose a model that aligns the user's cognition and affection at both the coarse-grained and fine-grounded levels and then automatically and manually evaluates the model. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates more empathetic and informative responses. |
Comparative evaluation of boundary-relaxed annotation for Entity Linking performance (2023.acl-long)
Copied to clipboard
| Challenge: | Entity Linking is a critical step for information extraction, allowing the retrieval and understanding of information from unstructured textual sources. |
| Approach: | They propose to use noisy datasets to generate noisy versions of annotated entity mentions and then train three Entity Linking models on this data. |
| Outcome: | The proposed model can be used to associate NE mentions to a single concept in an ontology, allowing for better indexing and relation extraction. |
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023? (2023.acl-long)
Copied to clipboard
| Challenge: | NER models trained on 20-year-old test set may not perform well on modern data. |
| Approach: | They evaluate the generalization of over 20 different models trained on the CoNLL-2003 dataset . they find no evidence of performance degradation in pre-trained Transformers . |
| Outcome: | The proposed model generalizations show that some models generalize well on new data while others do not. |
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises. |
| Approach: | They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications. |
| Outcome: | The proposed benchmarks are based on four different tasks and are designed to maximize diversity. |
MAD-TSC: A Multilingual Aligned News Dataset for Target-dependent Sentiment Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Sentiment classification is a task that requires domain-specific datasets. |
| Approach: | They propose a new dataset which includes aligned examples in eight languages . they show that machine translations can replace manual ones and that results match English . |
| Outcome: | The proposed dataset compares the performance of the proposed model with existing datasets in eight languages and human and machine translations. |
A New Dataset and Empirical Study for Sentence Simplification in Chinese (2023.acl-long)
Copied to clipboard
| Challenge: | Sentence simplification is a valuable technique that can benefit language learners and children. |
| Approach: | They propose a dataset for assessing sentence simplification in Chinese using manual simplifications from human annotators. |
| Outcome: | The proposed dataset shows that Chinese sentences are more accessible to children and nonnative readers than English sentences. |
Factual or Contextual? Disentangling Error Types in Entity Description Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation practices only distinguish between model generated referring expressions being accurate (ground-truth) versus inaccurate (not groundtruth). |
| Approach: | They propose to integrate indicators for factual inconsistencies and contextual incongruities into automated evaluations of language models to assess the differences in error types across familiar vs unfamiliar entities. |
| Outcome: | The proposed evaluation paradigm disentangles factuality and congruity errors in natural contexts. |
Weakly Supervised Vision-and-Language Pre-training with Relative Representations (2023.acl-long)
Copied to clipboard
| Challenge: | Weakly supervised vision-and-language pre-training (WVLP) uses only local descriptions of images as cross-modal anchors to construct weakly-aligned image-text pairs for pre- training. |
| Approach: | They propose to take a small number of aligned image-text pairs as anchors and represent each unaligned image and text by its similarities to these anchors. |
| Outcome: | The proposed model reduces the cost of pre-training while maintaining decent performance on downstream tasks. |
HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion (2023.acl-long)
Copied to clipboard
Wanrong He, Haoyu Dong, Yihuai Gao, Zhichao Fan, Xingzhuo Guo, Zhitao Hou, Xiao Lv, Ran Jia, Shi Han, Dongmei Zhang
| Challenge: | HermEs is a spreadsheet formula prediction language that is difficult for Excel users without programming experience to master. |
| Approach: | They propose a hierarchical approach to formula prediction via HiEraRchical forMulet ExpanSion . they propose generating formulas in a fixed order using hierarchically generated formulas . |
| Outcome: | The proposed approach improves formula prediction accuracy by guaranteeing correct grammar and streamlining token-level decoding with high-level Formulet. |
ArgU: A Controllable Factual Argument Generator (2023.acl-long)
Copied to clipboard
| Challenge: | Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. |
| Approach: | They propose a controllable neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure. |
| Outcome: | The proposed model produces factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. |
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show sentence-level extractive QA is outperformed by Generation-based QA (GenQA) models. |
| Approach: | They propose a training paradigm for GenQA using automatic QA evaluation models . they augment training data with answers generated by the GenQA model and labelled by GAVA . |
| Outcome: | The proposed training paradigm improves answering accuracy over existing models. |
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
| Approach: | They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. |
| Outcome: | The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations. |
Don’t Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Constituency parsing is a fundamental task in natural language processing, having many applications in downstream tasks such as language modeling. |
| Approach: | They propose a simple and unified approach for both continuous and discontinuous constituency parsing via autoregressive span selection. |
| Outcome: | The proposed model can predict all possible continuous and discontinuous constituency trees without sacrificing data coverage and without expensive chart-based parsing algorithms. |
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing (2023.acl-long)
Copied to clipboard
Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Fernandez Astudillo, Achille Fokoue, Tim Klinger
| Challenge: | Compositional generalization is a key feature of human intelligence and has been identified as a major point of weakness in neural methods for semantic parsing. |
| Approach: | They propose a neural parsing generation method that constructs logical forms from the bottom up, beginning from the logical form’s leaves. |
| Outcome: | The proposed method outperforms general-purpose parsers on a CFQ dataset and two other Text-to-SQL datasets while also being competitive with parser that have been tailored to each task. |
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge distillation methods focus on the transfer of model-specific knowledge but overlook data-specific information. |
| Approach: | They propose an attribution-driven knowledge distillation approach which explores the token-level rationale behind the teacher model and transfers attribution knowledge to the student model. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark and shows that it is more efficient than existing methods. |
(QA)2: Question Answering with Questionable Assumptions (2023.acl-long)
Copied to clipboard
| Challenge: | a number of questions contain questionable assumptions, such as when did Marie Curie discover Uranium, that cannot be answered as a true when question. |
| Approach: | They propose an open-domain evaluation dataset that can detect questionable assumptions . they propose a method that can be used to produce adequate responses for questions with questionable assumption. |
| Outcome: | The proposed model detects questionable assumptions and produces adequate responses for both types of questions. |
Attributable and Scalable Opinion Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization encode sentences from customer reviews into a hierarchical discrete latent space. |
| Approach: | They propose a method that encodes customer reviews into a hierarchical discrete latent space and then identifies common opinions based on their frequency. |
| Outcome: | The proposed method generates summaries that are more informative than previous work and more grounded in the input reviews. |
Targeted Data Generation: Finding and Fixing Model Weaknesses (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. |
| Approach: | They propose a framework that automatically identifies challenging subgroups and generates new data for those subgroup using large language models with a human in the loop. |
| Outcome: | The proposed framework improves accuracy on challenging subgroups while improving overall test accuracy. |
HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing paradigm to fine-tune parameters of pre-trained language models poses problems in data-scarce and resource-limited scenarios. |
| Approach: | They propose a parameter-efficient fine-tuning method HiFi that fine-tails only the highly informative and strongly correlated attention heads for the specific task. |
| Outcome: | The proposed method obtains state-of-the-art over the prior benchmarks on the GLUE benchmark. |
CFSum Coarse-to-Fine Contribution Network for Multimodal Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing multimodal summarization models ignore the contribution of visual modalities . we propose a novel contribution network to consider different contributions of images . |
| Approach: | They propose a Coarse-to-Fine contribution network for multimodal summarization to consider different contributions of images for summarizing. |
| Outcome: | The proposed system outperforms baselines on the visual and textual modalities. |
On “Scientific Debt” in NLP: A Case for More Rigour in Language Model Pre-Training Research (2023.acl-long)
Copied to clipboard
Made Nindyatama Nityasya, Haryo Wibowo, Alham Fikri Aji, Genta Winata, Radityo Eko Prasojo, Phil Blunsom, Adhiguna Kuncoro
| Challenge: | Despite rapid recent progress, current research practices conflate different sources of model improvement without conducting proper ablation studies and principled comparisons . authors conclude with recommendations for how to encourage and incentivize this line of work . |
| Approach: | They critique current research practices in the field of language model pre-training . they examine the success of language models pre-trained on large amounts of data . |
| Outcome: | The proposed models can achieve competitive or better performance than BERT under comparable conditions. |
End-to-end Knowledge Retrieval with Multi-modal Queries (2023.acl-long)
Copied to clipboard
| Challenge: | a new task is proposed to learn knowledge retrieval with multimodal queries . a vision-language model can retrieve knowledge using images and text inputs . |
| Approach: | They propose a task for vision-language models to retrieve knowledge with multi-modal queries . they propose reViz, a model that integrates content from both text and image queries based on a multimodal query task . |
| Outcome: | The proposed task performs better under zero-shot settings than previous work on cross-modal retrieval. |
AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)
Copied to clipboard
Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao
| Challenge: | Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech. |
| Approach: | They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness. |
| Outcome: | The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines . |
Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on multi-label intent detection are confused by the identical representation of the utterance with multiple labels and overlook the intrinsic intra-class and inter-class relations. |
| Approach: | They propose a dual class knowledge propagation network to learn well-separated representations for utterances with multiple intents. |
| Outcome: | The proposed method outperforms baselines on two multi-label intent datasets by a large margin. |
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets (2023.acl-long)
Copied to clipboard
| Challenge: | Anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. |
| Approach: | They propose an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements on seven public Darknet markets. |
| Outcome: | The proposed approach can help law enforcement agencies make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets. |
Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that automatic summarization generates concise summaries that contain key ideas of source documents. |
| Approach: | They propose to use Element-aware test sets to annotate news-related reference summaries to focus on more fine-grained news elements objectively and comprehensively. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 on the two datasets, respectively. |
Efficient Shapley Values Estimation by Amortization for Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Shapley Values are often estimated with a small number of stochastic model evaluations, but this can only be mitigated by aggregating thousands of model evaluation. |
| Approach: | They propose to combine a model with thousands of model evaluations to estimate Shapley Values without additional model evaluation. |
| Outcome: | The proposed model estimates Shapley Values accurately with up to 60 times speedup compared to traditional methods and does not suffer from stability issues as inference is deterministic. |
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results on ten datasets across seven domains demonstrate the effectiveness of PeerDA. |
| Approach: | They propose a new approach which uses span pairs with the PR relation as the augmentation data for training. |
| Outcome: | The proposed approach achieves state-of-the-art results on ten datasets across seven domains. |
Dynamic Regularization in UDA for Transformers in Multimodal Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Multimodal machine learning is a cutting-edge field that explores ways to combine information from multiple sources into models. |
| Approach: | They propose a multimodal BERT-ViT model that exploits weaker modality while regularizing the loss function. |
| Outcome: | The proposed model exploits weaker modality while regularizing the loss function. |
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing (2023.acl-long)
Copied to clipboard
| Challenge: | a growing body of work attempts to automatically detect media frames in the news or social media, but most adopts a topic-like view on frames, evading modelling the broader document-level narrative. |
| Approach: | They propose an annotation paradigm that breaks a complex annotation task into a series of simple binary questions. |
| Outcome: | The proposed method is both effective and transparent in its predictions. |
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark (2023.acl-long)
Copied to clipboard
Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Veselin Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev
| Challenge: | bgGLUE is a benchmark for evaluating language models on natural language understanding (NLU) tasks in Bulgarian. |
| Approach: | They propose to use a benchmark to evaluate language models on NLU tasks in Bulgarian. |
| Outcome: | The proposed model performs well on sequence labeling tasks, but there is room for improvement for tasks that require more complex reasoning. |
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to augment self-training (ST) in attribute-controllable language generation are limited and limited. |
| Approach: | They propose a new ST framework that integrates self-generated pseudo text into attribute-controllable language generation. |
| Outcome: | The proposed framework can be applied to semi-supervised controllable language generation. |
What does the Failure to Reason with “Respectively” in Zero/Few-Shot Settings Tell Us about Language Models? (2023.acl-long)
Copied to clipboard
| Challenge: | In the context of natural language inference, we examine how language models reason with respective readings from two perspectives: syntactic-semantic and commonsense-world knowledge. |
| Approach: | They propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResLI to encompass various explicit and implicit realizations of "respectively". |
| Outcome: | The proposed datasets include explicit and implicit readings of "respectively" the proposed dataset shows that fine-tuned models struggle with understanding readings without explicit supervision. |
BLIND: Bias Removal With No Demographics (2023.acl-long)
Copied to clipboard
| Challenge: | Numerous methods to mitigate social biases require prior knowledge of the demographics in the dataset, such as gender or race. |
| Approach: | They propose a method for bias removal without prior knowledge of demographics in the dataset. |
| Outcome: | Experiments with racial and gender biases in sentiment classification and occupation classification tasks show that BLIND mitigates biase . BLINT is competitive with methods that require demographic information and sometimes surpasses them. |
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text adversarial attacks are impractical in real-world scenarios where humans are involved. |
| Approach: | They have surveyed 378 human participants about the perceptibility of text adversarial examples produced by state-of-the-art methods. |
| Outcome: | The proposed methods ignore the property of imperceptibility or study it under limited conditions. |
Soft Alignment Objectives for Robust Adaptation of Language Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Domain adaptation is a common approach for generative language models, but it is notorious for over-specialization to the target domain, resulting in catastrophic forgetting. |
| Approach: | They propose to build training objectives on a semantic similarity of predicted tokens to the reference and avoid catastrophic forgetting of adaptation by preserving adaptation in-domain quality. |
| Outcome: | The proposed objectives mitigate catastrophic forgetting while preserving the adaptation in-domain quality while reducing computational costs. |
The CRINGE Loss: Learning what language not to model (2023.acl-long)
Copied to clipboard
| Challenge: | Standard language model training uses gold human documents or human-human interaction data and treats all training data as positive examples. |
| Approach: | They propose a procedure to train with negative examples using the "CRINGE" loss technique and use it to train models with such data. |
| Outcome: | The proposed procedure outperforms multiple strong baselines and is simple to train and implement. |
Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process (2023.acl-long)
Copied to clipboard
| Challenge: | Existing estimators measure performance by user satisfaction but ignore satisfaction dynamics across turns. |
| Approach: | They propose to use user satisfaction estimation to estimate performance of dialogue systems by using an estimator to simulate users. |
| Outcome: | The proposed estimator outperforms existing estimators on four benchmark dialogue datasets. |
Towards Identifying Fine-Grained Depression Symptoms from Memes (2023.acl-long)
Copied to clipboard
| Challenge: | Mental health disorders are a major economic burden for society and are projected to rise to a staggering US $6 trillion by 2030. |
| Approach: | They propose to use memes to identify fine-grained depression symptoms from memes . they benchmark RESTORE on 20 strong monomodal and multimodal methods . |
| Outcome: | The proposed method can predict fine-grained depression symptoms better than existing models that overlook implicit connections between visual and textual elements of a meme. |
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks (2023.acl-long)
Copied to clipboard
Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan S Sharma, Wei-Lun Wu, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding (SLU) tasks have received little attention and resources compared to lower-level tasks like speech and speaker recognition. |
| Approach: | They propose annotated SLU benchmark tasks based on freely available speech data to complement existing benchmarks and address gaps in the evaluation landscape. |
| Outcome: | The proposed benchmarks complement existing benchmarks and address gaps in the evaluation landscape. |
My side, your side and the evidence: Discovering aligned actor groups and the narratives they weave (2023.acl-long)
Copied to clipboard
| Challenge: | Identify distinct sets of aligned story actors responsible for sustaining issue-specific narratives . authors propose a novel two-step graph-based framework that identifies alignments between actors . |
| Approach: | They propose a proxy task to identify the distinct sets of aligned story actors . they propose identifying alignments between actors and extracting alignes using TAMPA . |
| Outcome: | The proposed framework is based on a corpus of text segments associated with six issues . it identifies aligned actors and extracts alignable actor groups from the network structure . |
Characterizing and Measuring Linguistic Dataset Drift (2023.acl-long)
Copied to clipboard
Tyler Chang, Kishaloy Halder, Neha Anna John, Yogarshi Vyas, Yassine Benajiba, Miguel Ballesteros, Dan Roth
| Challenge: | Existing metrics for dataset drift have not considered specific dimensions of linguistic drift that affect model performance. |
| Approach: | They propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift. |
| Outcome: | The proposed metrics are more effective than previous metrics at predicting out-of-domain model accuracies compared to popular fine-tuned embedding distances . |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
Copied to clipboard
Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
Synthesize, Prompt and Transfer: Zero-shot Conversational Question Generation with Pre-trained Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on QG focuses on generating single-turn questions, which are formalized as independent interactions. |
| Approach: | They propose a multi-stage knowledge transfer framework to leverage knowledge from single-turn question generation instances. |
| Outcome: | The proposed framework achieves 14.81 BLEU-4 (88.2% absolute improvement compared to T5) in CoQA with knowledge transferred from three single-turn datasets. |
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)
Copied to clipboard
Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolay Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister
| Challenge: | Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. |
| Approach: | They propose a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. |
| Outcome: | The proposed model achieves state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size. |
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies (2023.acl-long)
Copied to clipboard
| Challenge: | Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P. However, these systems still struggle in many openended generation settings, where they are asked to produce a long text following a short prompt. |
| Approach: | They propose to combine forward and reverse cross-entropy to train autoregressive language models by minimizing the cross-Entropy of the model distribution Q relative to the data distribution P. |
| Outcome: | The proposed model overgeneralizes and produces non-human-like text without complex decoding strategies. |
Knowledgeable Parameter Efficient Tuning Network for Commonsense Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing commonsense question answering models incur prohibitive computation costs and poor interpretability . |
| Approach: | They propose a parameter efficient tuning network to pair PLMs with external knowledge for commonsense question answering. |
| Outcome: | The proposed adapter integrates entity- and query-related knowledge at a small cost. |
BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric (2023.acl-long)
Copied to clipboard
Mingda Chen, Paul-Ambroise Duquenne, Pierre Andrews, Justine Kao, Alexandre Mourachko, Holger Schwenk, Marta R. Costa-jussà
| Challenge: | End-to-End speech-to speech translation is generally evaluated with text-based metrics . this means generated speech has to be automatically transcribed, making the evaluation dependent on ASR systems. |
| Approach: | They propose a text-free evaluation metric for end-to-end speech-tospeech translation, named BLASER, to avoid the dependency on automatic speech recognition systems. |
| Outcome: | The proposed metric avoids the dependency on automatic speech recognition systems by encoding generated speech segments into a shared embedding space. |
NLPositionality: Characterizing Design Biases of Datasets and Models (2023.acl-long)
Copied to clipboard
| Challenge: | Design biases in NLP systems often stem from creator’s positionality, i.e., views and lived experiences shaped by identity and background. |
| Approach: | They propose a framework for characterizing design biases and quantifying the positionality of NLP datasets and models. |
| Outcome: | The proposed framework characterizes design biases and quantifies alignment with dataset labels and model predictions. |
Backpack Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | We train a 170Mparameter Backpack language model on OpenWebText, matching the loss of a 6Bparameter Transformer. |
| Approach: | They propose a neural architecture that learns multiple non-contextual sense vectors for each word in a vocabulary and represents a word as a context-dependent, non-negative linear combination of sense vector. |
| Outcome: | The proposed model outperforms a GPT-2's word embeddings on lexical similarity evaluations and can be used to perform controllable text generation and debiasing. |
WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for measuring anti-LGBTQ+ bias are poorly defined and insufficiently grounded in real-world harms. |
| Approach: | They propose a bias benchmark that is community-sourced and generates a community survey. |
| Outcome: | The proposed method is community-sourced and improves on WinoQueer-v0. |
Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction. |
| Approach: | They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems . |
| Outcome: | The proposed framework outperforms baseline systems on the GMNER task. |
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference (2023.acl-long)
Copied to clipboard
Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
| Challenge: | Existing studies attribute catastrophic forgetting to fine-tuning, and they retain pre-trained knowledge indiscriminately without identifying what knowledge is transferable. |
| Approach: | They propose a unified objective for fine-tuning to retrieve the causality back from pre-trained data and use it to mitigate negative transfer while preserving knowledge. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuning methods on commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models. |
Translation-Enhanced Multilingual Text-to-Image Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for text-to-image generation are mostly based on the English language due to the lack of annotated image-caption data in other languages. |
| Approach: | They propose to use a multilingual multi-modal encoder to bootstrap mTTI systems that can be translated into other languages. |
| Outcome: | The proposed approach mitigates the language gap and improves on standard mTTI datasets. |
Benchmarking Large Language Model Capabilities for Conditional Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Autoregressive and pre-trained large language models have shifted the field from application-specific to generation-based approaches. |
| Approach: | They propose to adapt existing application-specific generation benchmarks to pre-trained large language models to better suit different tasks. |
| Outcome: | The proposed models differ in their applicability to different data regimes and their generalization to multiple languages. |
lilGym: Natural Language Visual Reasoning with Reinforcement Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to language-conditioned reinforcement learning in visual environments are limited by language semantics. |
| Approach: | They propose a new benchmark for language-conditioned reinforcement learning in visual environments . they annotate 2,661 highly-compositional human-written natural language statements . |
| Outcome: | The proposed approach is based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. |
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)
Copied to clipboard
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
| Challenge: | Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data. |
| Approach: | They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data. |
| Outcome: | The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models. |
Causality-aware Concept Extraction based on Knowledge-guided Prompting (2023.acl-long)
Copied to clipboard
| Challenge: | Concepts in knowledge graphs (KGs) are far from complete in existing knowledge graph models. |
| Approach: | They propose to equip a PLM-based extractor with a knowledge-guided prompt to alleviate concept bias by removing spurious co-occurrence correlations from existing knowledge. |
| Outcome: | The proposed prompt can alleviate concept bias and improve the performance of existing models. |
Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
| Outcome: | The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency . |
Limitations of Language Models in Arithmetic and Symbolic Induction (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has shown that large pretrained Language Models (LMs) can perform remarkably well on a range of NLP tasks but they have limitations on basic symbolic manipulation tasks such as copy, reverse, and addition. |
| Approach: | They propose to use explicit positional markers, fine-grained computation steps, and LMs with callable programs to teach large pretrained Language Models. |
| Outcome: | The proposed model can perform 100% accuracy in OOD and repeating symbols. |
EEL: Efficiently Encoding Lattices for Reranking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for decoding conditional text are slow to apply to large numbers of hypotheses. |
| Approach: | They propose a method that can efficiently encode lattices of generated outputs using Transformers. |
| Outcome: | The proposed method can extract high-quality hypotheses from lattices with minimal degradation error compared to naive reranking methods. |
CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks. |
| Approach: | They propose a cross-modal contrastive pre-training framework that learns from prosody variance of the same text token under different contexts. |
| Outcome: | The proposed framework can learn from prosody variance of a text token under different contexts. |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
Copied to clipboard
Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for learning dynamic contextualised word embeddings do not capture temporal semantic variations of words. |
| Approach: | They propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model using time-sensitive templates. |
| Outcome: | The proposed method significantly reduces the perplexity of test sentences in C2 outperforming the current state-of-the-art. |
How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech (2023.acl-long)
Copied to clipboard
| Challenge: | LSTMs and Transformers perform well at capturing the surface statistics of child-directed speech, but both model types generalize in a way consistent with an incorrect linear rule than the correct hierarchical rule. |
| Approach: | They train LSTMs and Transformers on text from the CHILDES corpus and evaluate what they learn about English yes/no questions. |
| Outcome: | The proposed models perform well at capturing the surface statistics of child-directed speech, but generalize more consistent with an incorrect linear rule than the correct hierarchical rule. |
GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator (2023.acl-long)
Copied to clipboard
Jian Yang, Shuming Ma, Li Dong, Shaohan Huang, Haoyang Huang, Yuwei Yin, Dongdong Zhang, Liqun Yang, Furu Wei, Zhoujun Li
| Challenge: | Existing pre-training methods underutilize the benefits of language understanding for generation. |
| Approach: | They propose a GAN-style model for encoder-decoder pre-training with an auxiliary discriminator. |
| Outcome: | The proposed model outperforms existing pre-trained models and achieves state-of-the-art performance. |
Log-linear Guardedness and its Implications (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for erasing human-interpretable concepts from neural representations that assume linearity are not fully understood. |
| Approach: | They define linear guardedness as the inability of an adversary to predict the concept directly from the representation . they show that a log-linear model can be constructed that indirectly recovers the concept . |
| Outcome: | The proposed model can be constructed that indirectly recovers the erased concept in some cases. |
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM’s Translation Capability (2023.acl-long)
Copied to clipboard
| Challenge: | Large multilingual language models exhibit impressive zero- or few-shot machine translation capabilities, despite never having been explicitly and intentionally exposed to translation data. |
| Approach: | They propose a mixed-method approach to measure and understand incidental bilingualism at scale using the Pathways Language Model. |
| Outcome: | The proposed model is exposed to over 30 million translation pairs across at least 44 languages. |
Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)
Copied to clipboard
Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang
| Challenge: | Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals. |
| Approach: | They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals. |
| Outcome: | The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations. |
Learning to Imagine: Visually-Augmented Natural Language Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for natural language generation are pre-trained on text-only corpora, resulting in visual commonsense. |
| Approach: | They propose a method that makes pre-trained language models learn to imagine for visually-augmented natural language generation. |
| Outcome: | The proposed method is compatible with Transformer-based architecture. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
Copied to clipboard
Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints (2023.acl-long)
Copied to clipboard
| Challenge: | Current approaches for conditional text generation focus on lexical constraints, but lack syntactic constraints to support complex semantic constraints. |
| Approach: | They propose a decoding algorithm that incorporates syntactic constraints to improve the quality of the generated text. |
| Outcome: | The proposed method improves on three different language generation tasks and shows improved lexical and syntactic metrics. |
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Prior studies have focused on translating utterances from high-resource languages to low-resourced languages. |
| Approach: | They propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set. |
| Outcome: | The proposed approach reduces errors and bias in the translated data, resulting in higher parser accuracies than the current model trained on machine translations. |
Ideology Prediction from Scarce and Biased Supervision: Learn to Disregard the “What” and Focus on the “How”! (2023.acl-long)
Copied to clipboard
| Challenge: | a novel supervised learning approach for political ideology prediction is needed for many applications. |
| Approach: | They propose a supervised learning approach for political ideology prediction that decomposes document embeddings into a linear superposition of two vectors. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with biased data with 5% accuracy. |
Unsupervised Extractive Summarization of Emotion Triggers (2023.acl-long)
Copied to clipboard
| Challenge: | Recent approaches trained supervised models to detect emotions and explain emotion triggers via abstractive summarization, but this can block necessary responses. |
| Approach: | They propose to augment an abstractive dataset with extractive triggers and develop unsupervised models that can jointly detect emotions and summarize their triggers. |
| Outcome: | The proposed model outperforms existing models and is based on a COVID-19 crisis dataset. |
Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. |
| Approach: | They propose a chain reasoning paradigm which captures long-range interdependence due to the chains’ compositional nature and generates decomposable first-order logic rules for reasoning. |
| Outcome: | The proposed method outperforms previous methods on two benchmarks and is robust enough to defend against adversarial attacks. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
Interpreting Positional Information in Perspective of Word Order (2023.acl-long)
Copied to clipboard
| Challenge: | Attention mechanism is a powerful and effective method utilized in natural language processing, but it is insensitive to positional information. |
| Approach: | They propose a weight concatenation operation to evaluate its efficacy in machine translation tasks. |
| Outcome: | The proposed operation can encode positional information and confirms our hypothesis. |
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation (2023.acl-long)
Copied to clipboard
Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
| Challenge: | Empirical results suggest that scale is not the only way to build commonsense capabilities. |
| Approach: | They propose a commonsense distillation framework that can achieve a competitive level of commonsensing without relying on the benefits of scale. |
| Outcome: | The proposed framework breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. |
More than Classification: A Unified Framework for Event Temporal Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for event temporal relation extraction ignore meaning of relations and wipe out their intrinsic dependency. |
| Approach: | They propose a unified event temporal relation extraction framework that transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time points. |
| Outcome: | The proposed framework outperforms the state-of-the-art model on TB-Dense and MATRES by 0.3% on both datasets. |
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Recent research on test-time adaptation suggests a possible way to improve the generalization ability of LLMs. |
| Approach: | They propose to use multi-armed bandit learning and multi-arm dueling bandits to solve a multi-source test-time model adaptation problem from user feedback. |
| Outcome: | The proposed model is more effective than other strong baselines on extractive question answering datasets. |
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for generalized intent discovery lack pseudo label disambiguation and representation learning. |
| Approach: | They propose a prototype learning framework to decouple pseudo label disambiguation and representation learning. |
| Outcome: | The proposed method can decouple pseudo label disambiguation and representation learning. |
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. |
| Approach: | They propose a metric that evaluates natural language generation tasks as an instruction-style question answering task and utilizes instruction-tuned pre-trained language models without training on evaluation datasets. |
| Outcome: | The proposed metric achieves state-of-the-art performance in untrained metrics for evaluating text summarization and dialogue generation, which exhibits strong dimension-level / task-level generalization ability and interpretability. |
Backdooring Neural Code Search (2023.acl-long)
Copied to clipboard
| Challenge: | Neural code search models are used to find code snippets from online repositories . however, their security aspect is rarely studied . |
| Approach: | They propose to use off-the-shelf code snippets from online repositories to find desired code . they propose to inject a backdoor into neural code search models which return buggy code if attacker modifies one variable/function name . |
| Outcome: | The proposed attack outperforms baselines on two neural code search models by 60%. |
Concise Answers to Complex Questions: Summarization of Long-form Answers (2023.acl-long)
Copied to clipboard
| Challenge: | Long-form question answering systems provide rich information by presenting paragraph-level answers, but not all information is required to answer the question. |
| Approach: | They propose an extract-and-decontextualize approach to summarize long-form answers using state-of-the-art models. |
| Outcome: | The proposed extract-and-decontextualize approach improves the quality of the extractive summary, exemplifying its potential in the summarization task. |
Towards Better Entity Linking with Multi-View Enhanced Distillation (2023.acl-long)
Copied to clipboard
Yi Liu, Yuan Tian, Jianxun Lian, Xinlong Wang, Yanan Cao, Fang Fang, Wen Zhang, Haizhen Huang, Weiwei Deng, Qi Zhang
| Challenge: | Entity linking is a fundamental task in Natural Language Processing (NLP), connecting mentions within unstructured contexts to their corresponding entities in a Knowledge Base (KB). |
| Approach: | They propose a dual-encoder framework that can efficiently match mentions to two-encoding frameworks by a global-view. |
| Outcome: | The proposed framework achieves state-of-the-art on several entity linking benchmarks. |
A Measure-Theoretic Characterization of Tight Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Language modeling is a core task in natural language processing. |
| Approach: | They propose to characterize leakage onto the set of infinite sequences by a measure-theoretic approach. |
| Outcome: | The proposed language model families are tight, meaning they will not leak . the proposed language models are based on the 'sequence leakage' hypothesis . |
PAED: Zero-Shot Persona Attribute Extraction in Dialogues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for persona attribute extraction from conversations are inconsistent and unreliable. |
| Approach: | They propose a model with a hard negative sampling strategy for generalized zero-shot persona attribute extraction. |
| Outcome: | The proposed model outperforms existing models in persona attribute extraction tasks. |
PromptRank: Unsupervised Keyphrase Extraction Using Prompt (2023.acl-long)
Copied to clipboard
| Challenge: | Existing keyphrase extraction methods struggle with document and candidate length discrepancies or fail to fully utilize the pre-trained language model without further fine-tuning. |
| Approach: | They propose an unsupervised keyphrase extraction approach that uses a pre-trained language model to rank candidates based on document embeddings. |
| Outcome: | The proposed approach outperforms the existing keyphrase extraction approach on six benchmarks. |
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. |
| Approach: | They propose a retrieval-augmentation method that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary. |
| Outcome: | The proposed method improves performance and reduces inference costs by only retrieving non-parametric memories when necessary. |
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information (2023.acl-long)
Copied to clipboard
| Challenge: | Several methods for characterizing datasets based on model-driven meta-information have been developed, but the relationship and complementary effects of these methods have received less attention. |
| Approach: | They propose a framework that captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. |
| Outcome: | The proposed framework outperforms baselines in three real-world applications and can be used in a variety of real-time problems. |
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models (2023.acl-long)
Copied to clipboard
Akshita Jha, Aida Mostafazadeh Davani, Chandan K Reddy, Shachi Dave, Vinodkumar Prabhakaran, Sunipa Dev
| Challenge: | Existing datasets on social stereotypes are limited in size and coverage . existing datasets are restricted to stereotypes prevalent in the Western society . |
| Approach: | They propose a broad-coverage stereotype dataset using generative models and a globally diverse rater pool to validate the prevalence of stereotypes in society. |
| Outcome: | The dataset validates the prevalence of stereotypes in society across 8 geo-political regions across 6 continents and states within the US and India. |
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations (2023.acl-long)
Copied to clipboard
Lucy Lu Wang, Yulia Otmakhova, Jay DeYoung, Thinh Hung Truong, Bailey Kuehl, Erin Bransom, Byron Wallace
| Challenge: | Prior work has shown that models may exploit shortcuts that are difficult to detect using standard n-gram similarity metrics such as ROUGE. |
| Approach: | They propose to use human-assessed summary quality facets and pairwise preferences to improve MDS evaluation methods. |
| Outcome: | The proposed methods improve the quality of literature review summarization models . they use human-assessed summary quality facets and pairwise preferences . |
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge. |
| Approach: | They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge. |
| Outcome: | The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions. |
An Inner Table Retriever for Robust Table Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Table Question Answering (TableQA) is a task of answering NL user questions using factoid answers extracted from table content. |
| Approach: | They propose a method for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. |
| Outcome: | The proposed method can improve TableQA's accuracy with up to 1.3-4.8% and achieve state-of-the-art in two benchmarks. |
SIMSUM: Document-level Text Simplification via Simultaneous Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level text simplification is a specific type of simplification which involves simplifying documents consisting of several sentences by rewriting them into fewer or more sentences. |
| Approach: | They propose a new two-stage framework SIMSUM for automated document-level text simplification which uses explicit summarization and simplification models and guides the generation using the main keywords of a source text. |
| Outcome: | The proposed model outperforms baseline models on two document-level simplification datasets, namely D-Wikipedia and Wiki-Doc. |
SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on large-scale corpora-based language models is limited and hard to generalize to all types of pre-trained language models. |
| Approach: | They propose a two-stage SimOAP strategy that over-samples and post-evaluates large-scale responses from existing models and selects a good response based on multiple evaluation metrics. |
| Outcome: | The proposed strategy outperforms baseline and automatic evaluation strategies in both automatic and human evaluations. |
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic (2023.acl-long)
Copied to clipboard
| Challenge: | Despite the recent advances in distributed representation and neural networks, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. |
| Approach: | They propose to use logic formalism to perform systematic attacks centring around natural logic to generate better adversarial examples with fewer visits to the victim models. |
| Outcome: | The proposed framework generates better adversarial examples with fewer visits to the victim models. |
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction (2023.acl-long)
Copied to clipboard
Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, Tim Althoff
| Challenge: | Psychotherapy can help people overcome negative thoughts by replacing them with a more hopeful "reframed thought" but clinician shortages and mental health stigma often limit access to therapy. |
| Approach: | They propose a framework of seven linguistic attributes that can be used to reframe a thought . they use a retrieval-enhanced in-context learning model to generate reframed thoughts . |
| Outcome: | The proposed model is based on a human-centered study of 600 situations, thoughts and reframes on 2,000 mental health websites. |
Dating Greek Papyri with Text Regression (2023.acl-long)
Copied to clipboard
John Pavlopoulos, Maria Konstantinidou, Isabelle Marthot-Santaniello, Holger Essler, Asimina Paparigopoulou
| Challenge: | a large number of Greek papyri documents can only be dated tentatively or in approximation due to the lack of decisive evidence. |
| Approach: | a new study trains regression models to estimate Greek papyri's date using a dataset of 389 transcriptions . the authors propose a method to estimate the date of 159 Greek pamphlets, which are only the upper limit known . |
| Outcome: | a new study predicts a date for Greek papyri with an average MAE of 54 years and an MSE of 1.17 . the model outperforms image classifiers and other baselines for 159 manuscripts, with only the upper limit known . |
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models generate natural language reasoning steps or Chains-of-Thoughts when prompted appropriately. |
| Approach: | They propose a new approach that interleaves retrieval with steps (sentences) in a CoT and uses retrieved results to improve CoT. |
| Outcome: | The proposed approach improves retrieval and downstream QA significantly on four datasets. |
Direct Fact Retrieval from Knowledge Graphs without Entity Linking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to retrieve facts from Knowledge Graphs (KGs) require additional labels and may accumulate errors . |
| Approach: | They propose a framework that directly retrieves facts from KGs given input text based on their representational similarities. |
| Outcome: | The proposed framework outperforms baselines on multiple fact retrieval tasks. |
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Question answering models have access to two sources of knowledge during inference time: parametric knowledge and contextual knowledge. |
| Approach: | They propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge. |
| Outcome: | The proposed model generates two answers for a given question based on parametric and contextual knowledge. |
A New Direction in Stance Detection: Target-Stance Extraction in the Wild (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for stance detection assume that the target is known in advance . Existing tasks use implicit mentions in the source text and are infeasible to have manual annotations at a large scale. |
| Approach: | They propose a task Target-Stance Extraction that aims to extract the (target, stance) pair from social media texts. |
| Outcome: | The proposed task can facilitate future research in the field of stance detection. |
Improved Instruction Ordering in Recipe-Grounded Conversation (2023.acl-long)
Copied to clipboard
| Challenge: | In this paper, we explore the task of instructional dialogue and focus on the cooking domain. |
| Approach: | They propose to explore two auxiliary subtasks to support response generation with improved instruction grounding by incorporating user intent and instruction state information into the model. |
| Outcome: | The proposed model lacks understanding of user intent and inability to track instruction state (i.e., which step was last instructed) incorporating user intent information helps the response generation model mitigate the incorrect order issue. |
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work on why Transformer-based large language models make predictions has made their behavior opaque due to the complexity of the computations performed within each layer. |
| Approach: | They propose a linear decomposition of final hidden states from autoregressive language models based on each initial input token, which is exact for virtually all contemporary Transformer architectures. |
| Outcome: | The proposed method analyzes the influence of input tokens on model probabilities over a sequence of upcoming words with only one forward pass from the model. |
Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level multi-event extraction aims to extract the structural information from a given document automatically. |
| Approach: | They propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two datasets with only a fraction of training time. |
Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)
Copied to clipboard
| Challenge: | a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks . |
| Approach: | They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively . |
| Outcome: | The proposed method outperforms existing models and achieves a 3.3% improvement on average. |
Language Detoxification with Attribute-Discriminative Latent Space (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to detoxify toxic text require excessive memory, computations and time. |
| Approach: | They propose a method to generate toxic text using an attribute-discriminative latent space. |
| Outcome: | The proposed method outperforms baselines on detoxified language and dialogue generation tasks while being time- and memory-efficient. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
Copied to clipboard
| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |
Adaptive and Personalized Exercise Generation for Online Language Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Empirical studies have shown various benefits of adaptive learning, such as improved student learning outcomes, lower dropout rates, and increased instructor satisfaction. |
| Approach: | They propose to combine a knowledge tracing model that estimates each student’s evolving knowledge states from their learning history with a controlled text generation model that generates exercise sentences based on the student’ s current estimated knowledge state and instructor requirements of desired properties. |
| Outcome: | The proposed model can generate superior exercises based on student state and instructor requirements . Empirical studies have shown that adaptive learning improves student learning outcomes, lower dropout rates, and increased instructor satisfaction. |
NLP Reproducibility For All: Understanding Experiences of Beginners (2023.acl-long)
Copied to clipboard
| Challenge: | a study with 93 students in an introductory NLP class shows that beginners' programming skill and comprehension of research papers have a limited impact on their effort spent completing the exercise. |
| Approach: | a study conducted with 93 students in an introductory NLP course questioned them on their programming background and programming background. |
| Outcome: | The results show that beginners' programming skill and comprehension of research papers have a limited impact on their effort spent on the exercise. |
Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA (2023.acl-long)
Copied to clipboard
| Challenge: | Visual question answering models seek to answer questions about images . ambiguity can exist at all levels of linguistic analysis, but disagreements can be difficult to detect and resolve . |
| Approach: | They develop a question-generation model which integrates group information without supervision and uses a dataset of ambiguous examples to annotate answers. |
| Outcome: | The proposed model can integrate answer group information without supervision and is able to fill knowledge gaps and convey requests. |
UMRSpell: Unifying the Detection and Correction Parts of Pre-trained Models towards Chinese Missing, Redundant, and Spelling Correction (2023.acl-long)
Copied to clipboard
| Challenge: | Chinese Spelling Correction (CSC) is a task of detecting and correcting misspelled charac- ters in Chinese texts. |
| Approach: | They propose a model to learn detection and correction parts together from a multi-task learning perspective. |
| Outcome: | The proposed model can learn detection and correction parts together from a multi-task learning perspective. |
LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction (2023.acl-long)
Copied to clipboard
Jeremiah Milbauer, Annie Louis, Mohammad Javad Hosseini, Alex Fabrikant, Donald Metzler, Tal Schuster
| Challenge: | In many NLP tasks, the input text can be seen as a sequence of related segments. |
| Approach: | They propose a layer-adjustable interactions framework that contextualizes token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length. |
| Outcome: | The proposed model reduces 30-50% of attention FLOPs while maintaining high accuracy. |
Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features. |
| Approach: | They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations. |
| Outcome: | The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation. |
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions (2023.acl-long)
Copied to clipboard
| Challenge: | Modern machine learning relies on datasets to develop and validate research ideas. |
| Approach: | They propose a dataset recommendation system that uses a training set and an evaluation set to help people find relevant datasets. |
| Outcome: | The proposed model finds more relevant search results than existing third-party search engines. |
Multilingual Event Extraction from Historical Newspaper Adverts (2023.acl-long)
Copied to clipboard
| Challenge: | Developing NLP methods for historical corpora is difficult, as only domain experts can label them . off-the-shelf models are trained on modern language texts, rendering them weaker for historical documents . |
| Approach: | They propose to use an annotated newspaper dataset to extract historical data from a novel domain of texts. |
| Outcome: | The proposed method performs well on a multilingual dataset in English, French, and Dutch . it is possible to extract surprisingly good results even with scarce annotated data using existing models and datasets for modern languages . |
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)
Copied to clipboard
Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
| Challenge: | Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution. |
| Approach: | They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work. |
Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions (2023.acl-long)
Copied to clipboard
| Challenge: | missing facts, incomplete schema and limited scope lead to many questions being unanswerable. |
| Approach: | They propose to adapt a KBQA dataset with unanswerable questions to detect missing facts and incomplete schema. |
| Outcome: | The proposed model performs poorly even after adaptation for unanswerable questions. |
Understanding Client Reactions in Online Mental Health Counseling (2023.acl-long)
Copied to clipboard
| Challenge: | Communication success relies heavily on reading participants’ reactions, but little research is on how listeners' reactions shape trajectories and outcomes of conversations. |
| Approach: | They propose to use client reactions to predict counseling outcomes by using an annotation framework that encompasses counselors’ strategies and client reaction behaviors. |
| Outcome: | The proposed framework can predict counselors' strategies and client reaction behaviors against a large-scale text-based counseling dataset. |
Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Existing topic models assume that topics are independent and that they are not a tree structure, which complicates the analysis. |
| Approach: | They propose a neural topic model with a Gaussian mixture prior distribution to improve the model’s ability to adapt to sparse data. |
| Outcome: | The proposed model outperforms baseline models on sparse data on a set of widely used datasets and generates more coherent topics and rational topic structures. |
Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)
Copied to clipboard
| Challenge: | Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT. |
| Approach: | They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers. |
| Outcome: | The proposed method achieves consistent and significant performance gains across all tasks and model sizes. |
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers (2023.acl-long)
Copied to clipboard
Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch
| Challenge: | Existing approaches to apply language models to tasks that require intermediate representations are less informative. |
| Approach: | They propose a novel approach that utilizes the contrast between layers to improve text generation outputs. |
| Outcome: | The proposed approach mitigates degenerative behaviors of the model in open-ended generation, significantly improving the quality of generated texts. |
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering (2023.acl-long)
Copied to clipboard
Anku Rani, S.M Towhidul Islam Tonmoy, Dwip Dalal, Shreya Gautam, Megha Chakraborty, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Contemporary fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. |
| Approach: | They propose a 5W framework for question-answer-based fact explainability that can assist human fact-checkers in asking relevant questions . they propose masked language model which generates QA pairs for claims and a baseline QA system that automatically locates those answers from evidence documents. |
| Outcome: | The proposed framework can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. |
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
Copied to clipboard
Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families. |
| Outcome: | The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. |
CREPE: Open-Domain Question Answering with False Presuppositions (2023.acl-long)
Copied to clipboard
| Challenge: | Existing question answering datasets assume all questions have well defined answers. |
| Approach: | They propose a QA dataset containing a distribution of false presuppositions . they find that 25% of questions contain false presumptions . |
| Outcome: | The proposed model finds that 25% of questions contain false presuppositions . the model can find presuffpositions moderately well, but struggle when predicting correctness . |
Joint Document-Level Event Extraction via Token-Token Bidirectional Event Completed Graph (2023.acl-long)
Copied to clipboard
| Challenge: | a joint exaction method can be used to extract document-level event records . it avoids inefficiency and error propagation issues in traditional pipeline methods . |
| Approach: | They propose a joint exaction method that can avoid inefficiency and error propagation issues . they propose eType-Role1-Roul2 as the edge type to reveal which tokens play argument roles . |
| Outcome: | The proposed method can avoid inefficiency and error propagation issues in traditional pipeline methods. |
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to short text clustering are prone to degenerate solutions and noisy data. |
| Approach: | They propose a model to improve robustness against imbalanced and noisy data . they propose self-adaptive optimal transport and class-wise contrastive learning . |
| Outcome: | The proposed model outperforms the state-of-the-art models on eight short text clustering datasets. |
Multilingual Knowledge Graph Completion with Language-Sensitive Multi-Graph Attention (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multilingual knowledge graph completion have two drawbacks: alignment dependency and training inefficiency. |
| Approach: | They propose a multilingual knowledge graph completion framework with language-sensitive multi-graph attention to predict missing links on all given KGs. |
| Outcome: | The proposed model improves on the DBP-5L and E-PKG datasets. |
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization (2023.acl-long)
Copied to clipboard
Griffin Adams, Bichlien Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad
| Challenge: | Summarization models are trained to maximize the likelihood of a single reference (MLE) but little is known about why one setup is more effective than another . |
| Approach: | They add a calibration step which exposes a model to its own ranked outputs to improve relevance or contrasts positive and negative sets to improve faithfulness. |
| Outcome: | The proposed calibration step can unlock large gains in relevance or faithfulness. |
Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution (2023.acl-long)
Copied to clipboard
| Challenge: | Recent results show that annotating mentions is twice as fast as annotation of full coreference chains. |
| Approach: | They propose a method for efficiently adapting coreference models using only mentions in the target domain without increasing annotator time. |
| Outcome: | The proposed method improves average F1 without increasing annotator time. |
A Universal Discriminator for Zero-Shot Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | Generative modeling has been the dominant approach for large-scale pretraining and zeroshot generalization. |
| Approach: | They propose a discriminator that predicts whether a text sample comes from the true data distribution and which option has the highest probability of coming from the real data distribution. |
| Outcome: | The proposed discriminative approach outperforms GANs on a number of NLP tasks by 16.0%, 7.8%, and 11.5% respectively. |
Syntax and Geometry of Information (2023.acl-long)
Copied to clipboard
| Challenge: | a new information-theoretical model of syntactic generalization is proposed . abstract optimization is a relaxation of the property of independence . |
| Approach: | They propose to model syntactic generalization from the perspective of the capacity to disentangle semantic and structural information. |
| Outcome: | The proposed model outperforms other methods on the task of inducing syntactic categories from natural language data. |
GreenKGC: A Lightweight Knowledge Graph Completion Method (2023.acl-long)
Copied to clipboard
| Challenge: | Knowledge graph completion (KGC) aims to discover missing relationships in knowledge graphs (KGs). |
| Approach: | They propose a modularized knowledge graph completion solution that learns embeddings for entities and relations through a score function. |
| Outcome: | Experimental results show that GreenKGC outperforms SOTA methods in low dimensions and even better against high-dimensional models with a much smaller model size. |
Unsupervised Open-domain Keyphrase Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models that generate keyphrases without human-labeled data are lacking in this area. |
| Approach: | They propose a model that consists of two modules that can be built in an unsupervised fashion and can perform consistently across domains. |
| Outcome: | The proposed model performs consistently across domains and narrows the gap between supervised and unsupervised models down to about 16%. |
A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment (2023.acl-long)
Copied to clipboard
| Challenge: | Existing cognitive stimulation systems lack data on how to integrate emotional support and therapy principles into chit-chat dialogue systems. |
| Approach: | They propose a multi-source knowledge fusion method for CS dialogue to generate open-ended responses guided by the therapy principle and emotional support strategy. |
| Outcome: | The proposed method generates open-ended responses guided by the therapy principle and emotional support strategy of the target response. |
Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)
Copied to clipboard
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases. |
| Approach: | They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. |
| Outcome: | The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen. |
Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Out-of-domain (OOD) intent classification is an active field of natural language understanding . previous studies have suggested that PTMs would be "overthinking" the semantic features of the sample in the open-world scenario . |
| Approach: | They propose a method that allows the model to decide whether to make a decision on OOD classification early during inference. |
| Outcome: | The proposed method can improve inference speed and achieve significant performance improvements. |
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work on document-level simplification has focused on sentence-level edits, while many desirable edits require document- level context. |
| Approach: | They propose a dataset that reconstructs the document-level editing process from English Wikipedia to paired Simple Wikipedia articles. |
| Outcome: | The proposed dataset reconstructs the document-level editing process from English Wikipedia (EW) articles to paired Simple Wikipedia (SEW) pages. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
A dynamic programming algorithm for span-based nested named-entity recognition in O(n2) (2023.acl-long)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a fundamental problem in information retrieval . nested NER has a cubic-time complexity, but can be realized in quadratic time using a semi-Markov model . |
| Approach: | They propose a span-based named-entity recognition algorithm with a quadratic-time complexity . they add a constraint on the search space to reduce the complexity of the algorithm . |
| Outcome: | The proposed algorithm covers a large part of three standard English benchmarks and delivers comparable results. |
Target-Side Augmentation for Document-Level Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data. |
| Approach: | They propose a document-level machine translation model that generates many potential translations for each source document and smoothes the distribution. |
| Outcome: | The proposed method outperforms the previous best system by 2.30 s-BLEU on News and achieves new state-of-the-art on News . |
Rethinking Masked Language Modeling for Chinese Spelling Correction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing CSC models over-fit the error model while under-fitting the language model, resulting in poor generalization to out-of-distribution error patterns. |
| Approach: | They propose to use a multi-domain benchmark LEMON to assess the open domain generalization of Chinese Spelling Correction models. |
| Outcome: | The proposed method achieves state-of-the-art results on SIGHAN, ECSpell, and LEMON. |
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for conditional inference on joint textual and visual clues lack multimodal context reasoning capability. |
| Approach: | They propose a multi-modal context reasoning approach that embeds textual semantics and objective image information into the pretrained language model to perform context reasoning. |
| Outcome: | The proposed approach improves on two data sets and shows 4.8% gain on the PMR. |
Simple and Effective Unsupervised Speech Translation (2023.acl-long)
Copied to clipboard
Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
| Approach: | They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis. |
| Outcome: | The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions. |
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate conversational question are naive and do not account for the answer span. |
| Approach: | They propose a framework for generating a conversational question from a context. |
| Outcome: | The proposed framework achieves state-of-the-art in two different settings compared to existing models . it uses a sentence as the rationale and extracts the answer span from it . |
CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures. |
| Approach: | They propose a document-level event causality identification model which annotates central events and incorporates event centrality information into the reasoning network. |
| Outcome: | The proposed model performs high-order reasoning while considering event centrality. |
f-Divergence Minimization for Sequence-Level Knowledge Distillation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge distillation approaches focus on minimizing a generalized f-divergence function. |
| Approach: | They propose a framework which formulates sequence-level knowledge distillation as minimizing a generalized f-divergence function. |
| Outcome: | The proposed framework outperforms existing methods and reduces intractable divergence to word-level losses. |
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to recognize emotions have limitations in discovering the intrinsic structure of data relevant to emotion labels, and struggle to extract generalized and robust representations. |
| Approach: | They propose a supervised adversarial contrastive learning framework for learning class-spread structured representations in a controlled manner. |
| Outcome: | The proposed framework can extract generalized and robust representations on three datasets and achieves state-of-the-art performance. |
A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
| Outcome: | The proposed model surpasses existing methods by a large margin and achieves state-of-the-art results on a document-level relation extraction dataset. |
A Synthetic Data Generation Framework for Grounded Dialogues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to train grounded dialogues require large amounts of data. |
| Approach: | They propose a synthetic data generation framework for grounded dialogues that takes knowledge data and heuristics to determine a dialogue flow and incrementally turn it into a dialog. |
| Outcome: | The proposed framework significantly boosts model performance in training data and low-resource scenarios. |
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages (2023.acl-long)
Copied to clipboard
Cheikh M. Bamba Dione, David Ifeoluwa Adelani, Peter Nabende, Jesujoba Alabi, Thapelo Sindane, Happy Buzaaba, Shamsuddeen Hassan Muhammad, Chris Chinenye Emezue, Perez Ogayo, Anuoluwapo Aremu, Catherine Gitau, Derguene Mbaye, Jonathan Mukiibi, Blessing Sibanda, Bonaventure F. P. Dossou, Andiswa Bukula, Rooweither Mabuya, Allahsera Auguste Tapo, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Fatoumata Ouoba Kabore, Amelia Taylor, Godson Kalipe, Tebogo Macucwa, Vukosi Marivate, Tajuddeen Gwadabe, Mboning Tchiaze Elvis, Ikechukwu Onyenwe, Gratien Atindogbe, Tolulope Adelani, Idris Akinade, Olanrewaju Samuel, Marien Nahimana, Théogène Musabeyezu, Emile Niyomutabazi, Ester Chimhenga, Kudzai Gotosa, Patrick Mizha, Apelete Agbolo, Seydou Traore, Chinedu Uchechukwu, Aliyu Yusuf, Muhammad Abdullahi, Dietrich Klakow
| Challenge: | POS tagging is one of the fundamental steps for many natural language processing (NLP) applications. |
| Approach: | They present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. |
| Outcome: | The proposed model improves POS tagging performance in unseen languages. |
Semantic Structure Enhanced Event Causality Identification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events. |
| Approach: | They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator. |
| Outcome: | The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations. |
Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries . |
| Approach: | They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations. |
| Outcome: | The proposed framework is more efficient than existing methods. |
Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)
Copied to clipboard
Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han, Ling Wang, Xu Shao, Wenqiu Zeng
| Challenge: | Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging. |
| Approach: | They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model. |
| Outcome: | The proposed method is comparable to existing methods and comparable to those using historical data. |
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models only address text classification problem in the euclidean space, which is not optimal . e.g., fear and terrified labels may not be differentiated in such space, harming performance . |
| Approach: | They propose a framework that can integrate hyperbolic embeddings to improve the task . they learn label embeddements in the hyperbolical space and then add them to the framework . |
| Outcome: | The proposed framework improves fine-grained emotion classification on two benchmark datasets with 3% improvement over previous state-of-the-art models. |
Combo of Thinking and Observing for Outside-Knowledge VQA (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to visual question answering use external knowledge to acquire and use knowledge beyond images. |
| Approach: | They propose to constrain the cross-modality space into the same space of natural-language space . they propose a multimodal encoder, textual encoder and answer decoder to introduce more types of knowledge . |
| Outcome: | The proposed framework outperforms the state-of-the-art by 6.17% accuracy on a cross-modal space and natural-language space. |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
| Approach: | They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model. |
| Outcome: | The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation. |
Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for identifying offensive content in interpersonal communication are largely independent of context . prior work has shown the benefits of modeling context, such as demographics of annotators and readers, and the online community in which a message is said. |
| Approach: | They propose a model that explicitly models the social context in which a message is said to assess whether it is appropriate. |
| Outcome: | The proposed model can accurately identify inappropriate communication in a given context. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning paradigms that focus on large corpus are limiting compositional generalization performance. |
| Approach: | They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case . |
| Outcome: | The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples . |
Attractive Storyteller: Stylized Visual Storytelling with Unpaired Text (2023.acl-long)
Copied to clipboard
| Challenge: | Xu et al., 2015; Guo e t al, 2022a) focus on generating objective and neutral descriptions of image content without considering style characteristics. |
| Approach: | They propose a task of Stylized Visual Storytelling to generate attractive stylized stories for a photo stream. |
| Outcome: | The proposed framework can generate attractive stories with different styles . it surpasses state-of-the-art methods on automatic and human evaluation metrics. |
Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation (2023.acl-long)
Copied to clipboard
Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma
| Challenge: | Existing methods for learning representations of structured knowledge are limited to the minority of people with technical skills. |
| Approach: | They propose a large pretraining dataset and strategy for learning representations of text, tables, and SQL code that leverages the entire context of the problem. |
| Outcome: | The proposed model improves on two SQL tasks and shows a 1.7 and 2.2 percentage point improvement over existing methods. |
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing word alignment methods rely on manual data and lack generalization ability. |
| Approach: | They propose to use a weakly-supervised large-scale weakly supervised dataset for word alignment pre-training via span prediction to reduce the need for manual data. |
| Outcome: | The proposed method improves upon the best supervised baseline by 3.3 6.1 points in F1 and 1.5 6.1 point in AER. |
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models (2023.acl-long)
Copied to clipboard
| Challenge: | Increasing the size of pre-trained models can consistently improve performance on downstream tasks after fine-tuning, as seen in studies based on BERT, RoBERTa, T5 and empirical scaling laws. |
| Approach: | They propose to use knowledge distillation to build a compact model with a fixed budget instead of annotating data and manually labeling it. |
| Outcome: | The proposed approach reduces inference costs by reducing costs by hiring annotators and labelling data. |
OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing pipelines for relational triple extraction are underutilizing regional information of triple. |
| Approach: | They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection . |
| Outcome: | The proposed framework could extract all types of triples on two widely used datasets. |
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons (2023.acl-long)
Copied to clipboard
Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu
| Challenge: | Existing dialogue agents, while able to produce human-like responses, often do not model goal-driven and grounded language interactions. |
| Approach: | They propose to decompose and model teacher-student natural language interactions into (1) the DM’s intent to guide players toward a given goal; (2) the dm’s guidance utterance to the players expressing this intent; (3) a theory-of-mind model that anticipates the players’ reaction to the guidance one turn into the future. |
| Outcome: | The proposed task is based on a goal-driven and grounded environment with a teacher-student interaction model and theory-of-mind model. |
Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks. |
| Approach: | They propose to combine pre-trained modules with pre-trains to boost prompt tuning for few-shot learning. |
| Outcome: | The proposed model outperforms prompt tuning, full model tuning, and prior prompt pre-training methods in few-shot learning settings. |
Is GPT-3 a Good Data Annotator? (2023.acl-long)
Copied to clipboard
| Challenge: | Data annotation is the process of labeling data that could be used to train machine learning models. |
| Approach: | They evaluate the performance of a large-scale language model developed by OpenAI . they compare it with traditional methods and analyze its output on a range of tasks . |
| Outcome: | The proposed model has shown impressive performance on a range of NLP tasks. |
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (2023.acl-long)
Copied to clipboard
| Challenge: | Existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses. |
| Approach: | They propose a multi-grained knowledge retrieval system that decouples knowledge retrievals from response generation and introduces an entity selector and an attribute selector to acquire multigrained information from the knowledge base. |
| Outcome: | The proposed system performs better on small and large knowledge bases. |
Few-shot Event Detection: An Empirical Study and a Unified View (2023.acl-long)
Copied to clipboard
| Challenge: | Extensive studies have been carried out on fewshot event detection (ED) however, there are noticeable discrepancies among existing methods from three aspects. |
| Approach: | They propose a unified view of ED models and a better unified baseline for fair evaluation. |
| Outcome: | The proposed framework outperforms existing methods by a large margin on three datasets. |
How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study found that pre-training can teach language models to rely on hierarchical syntactic features . aaron ramirez: we find that pretraining on simpler language induces a hierarchic bias . |
| Approach: | They find that pre-training can teach language models to rely on hierarchical syntactic features . authors: this suggests that in cognitively plausible language acquisition settings, models may be more data-efficient . |
| Outcome: | a recent study shows that pre-training can teach language models to rely on hierarchical features . the findings suggest that in plausible language acquisition settings, language models may be more data-efficient than previously thought . |
ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations (2023.acl-long)
Copied to clipboard
Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, Chandra Bhagavatula
| Challenge: | Changing contexts can flip the moral judgment of an action. |
| Approach: | They propose an interactive system that learns to ask clarification questions to elicit salient contexts of a social or moral situation. |
| Outcome: | The proposed system generates more relevant, informative and defeasible questions compared to baselines. |
HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent NLP models have shown the remarkable ability to generalise ‘zero-shot’ to new tasks using only natural language instructions as guidance. |
| Approach: | They introduce Hypernetworks for INstruction Tuning (HINT) which converts task instructions and examples into parameter-efficient modules inserted into an underlying model using a pretrained text encoder. |
| Outcome: | The proposed models outperform strong state-of-the-art models by over 10% when controlling for compute. |
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning is an important paradigm for adapting large language models to new tasks . but the generalization behavior of ICL remains poorly understood . |
| Approach: | They characterize the feature biases of large language models by constructing underspecified demonstrations . they find that LLMs exhibit clear feature bias, and they evaluate interventions . |
| Outcome: | The proposed model prefers the "default" task features over distractor features more often than the base model. |
An Inclusive Notion of Text (2023.acl-long)
Copied to clipboard
| Challenge: | despite its central role, the notion of text in natural language processing is vague, authors argue . a conceptual framework for capturing text differences is lacking, authors say . authors propose a two-tier taxonomy of linguistic and non-linguistic elements available in textual sources . |
| Approach: | They propose a taxonomy of linguistic and non-linguistic elements available in textual sources and can be used in NLP modeling. |
| Outcome: | The proposed taxonomy examines the production and transformation of textual data . it outlines key desiderata and challenges of the emerging inclusive approach to text in NLP . |
AlignScore: Evaluating Factual Consistency with A Unified Alignment Function (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to evaluate factual consistency of text depend on limited data . e.g., generated text can contain factual inconsistencies that are irrelevant to context . |
| Approach: | They propose a new holistic metric that measures factual inconsistencies . they use 4.7M training examples from 7 well-established tasks . |
| Outcome: | The proposed metric outperforms existing metrics on 22 datasets and matches or outperFORMs them. |
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies can only identify sarcastic post but could not give concrete explanation for why it is sarkastic. |
| Approach: | They propose a multimodal sarcasm explanation scheme that generates a sentence for a social post to explain why it contains sarkasis. |
| Outcome: | The proposed model outperforms existing methods on a public dataset. |
Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
Multi-granularity Temporal Question Answering over Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on temporal knowledge graphs ignores fact that real-life applications of TKGQA are complex in temporal granularity. |
| Approach: | They propose a large scale dataset for multi-granularity temporal question answering over knowledge graphs . they propose comparing MultiQA over MultiTQ to better reflect real-world challenges . |
| Outcome: | The proposed dataset is among the first of its kind and features multiple temporal granularities. |
A New Aligned Simple German Corpus (2023.acl-long)
Copied to clipboard
| Challenge: | “Leichte Sprache” is a regulated language aimed to facilitate complex written language that would otherwise stay inaccessible to different groups of people. |
| Approach: | They propose to use automatic sentence-alignment methods to align multiple document-aligned sources to improve their sentence alignments. |
| Outcome: | The proposed dataset outperforms previous work and is available under MIT license. |
Introducing Semantics into Speech Encoders (2023.acl-long)
Copied to clipboard
Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
Constrained Tuple Extraction with Interaction-Aware Network (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge triples lack constraints for their authenticity due to spatial, temporal, or other constraints. |
| Approach: | They propose a constrained tuple extraction task to guarantee the validity of knowledge tifles by using an interaction-aware network to extract constrained text. |
| Outcome: | The proposed model outperforms existing models on the dataset and the public CaRB dataset. |
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show zero-shot performance on unseen multimodal tasks . instruction tuning has yet to be explored for vision and multimodal task. |
| Approach: | They propose a multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. |
| Outcome: | The proposed model performs well on unseen multimodal tasks and is highly scalable. |
Single Sequence Prediction over Reasoning Graphs for Multi-hop QA (2023.acl-long)
Copied to clipboard
| Challenge: | Recent generative approaches for multi-hop question answering (QA) use fusion-in-decoder to generate a single sequence output . but, they often have difficulty accurately identifying passages corresponding to key entities in the context . |
| Approach: | They propose a single-sequence prediction method that integrates a graph structure linking key entities in each context passage to relevant subsequent passages for each question. |
| Outcome: | The proposed method improves answer exact-match/F1 scores and faithfulness of grounding on the hotpotQA dataset and achieves state-of-the-art numbers on the Musique dataset. |
Contrastive Error Attribution for Finetuned Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for error tracing do not detect faithfulness errors in NLG datasets. |
| Approach: | They propose a framework to identify and remove low-quality training instances that lead to undesirable outputs. |
| Outcome: | The proposed method outperforms existing methods for detecting faithfulness errors in NLG datasets. |
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications (2023.acl-long)
Copied to clipboard
| Challenge: | Several explainability methods have been shown to be brittle in the face of adversarial perturbations of their inputs in the image and generic textual domains. |
| Approach: | They propose to adapt existing attribution robustness estimation methods to take into account domain-specific plausibility and to train networks that display robust attributions. |
| Outcome: | The proposed methods are able to characterize domain-specific plausibility and provide robust explanations on biomedical datasets. |
Neural Machine Translation for Mathematical Formulae (2023.acl-long)
Copied to clipboard
| Challenge: | a recent paper examines the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. |
| Approach: | They perform translation tasks from LaTeX to Mathematica and from La TeX into semantic LaTaX using convolutional sequence-to-sequence networks. |
| Outcome: | The proposed translations achieve 95.1% and 90.7% exact matches between the two languages. |
Query-Efficient Black-Box Red Teaming via Bayesian Optimization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for generating test cases and querying fail to be query-efficient . generative models can be used for open-domain dialogue, prompt continuation, text-to-image generation . |
| Approach: | They propose a query-efficient method that iteratively finds diverse positive test cases leading to model failures by utilizing user input and past evaluations. |
| Outcome: | The proposed method finds a significantly larger number of diverse positive test cases under limited query budget than baseline methods. |
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control (2023.acl-long)
Copied to clipboard
| Challenge: | Existing diffusion models for continuous-valued domains have not been adopted for text data. |
| Approach: | They propose a diffusion-based language model with two key design choices . semi-autoregressive model generates blocks of text and allows local context updates . they evaluate it on unconstrained text generation benchmarks . |
| Outcome: | The proposed model outperforms autoregressive models on unconstrained text generation benchmarks on uncontrolled text generation. |
Recall, Expand, and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing (2023.acl-long)
Copied to clipboard
| Challenge: | State-of-the-art (SOTA) methods use the cross-encoder architecture to concatenate a mention (and its context) with each type and feed it into a pretrained language model (PLM) to score their relevance. |
| Approach: | They propose to perform entity typing in a recall-expand-filter manner and use a novel model to encode and score all these K candidates in one forward pass. |
| Outcome: | The proposed method is thousands of times faster than the CE-based architecture and is very efficient in fine-grained (130 types) and coarse-grain (9 types) entity typing. |
MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Audio-visual speech recognition (AVSR) leverages multimodal signals to understand human speech. |
| Approach: | They propose an adversarial network to refine frame-level modality-invariant representations to bridge the distribution gap between modalities. |
| Outcome: | The proposed approach outperforms the state-of-the-art on public benchmarks LRS3 and LRS2 on the modalities of AVSR. |
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors (2023.acl-long)
Copied to clipboard
Liyan Tang, Tanya Goyal, Alex Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kryscinski, Justin Rousseau, Greg Durrett
| Challenge: | Abstractive summarization systems still include factual errors in generated summaries despite recent improvements in factuality detection . |
| Approach: | They aggregate factuality error annotations from nine existing datasets and stratify them according to the underlying summarization model. |
| Outcome: | The proposed method improves on the ChatGPT-based model and shows that it is not superior for all error types. |
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods on understanding multi-party conversations typically embed interlocutors and utterances into sequential information flows or use superficial graph structures. |
| Approach: | They propose a plug-and-play method which adapts Transformer-based pre-trained language models for universal MPC understanding. |
| Outcome: | The proposed method can adapt Transformer-based pre-trained language models for universal MPC understanding. |
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks (2023.acl-long)
Copied to clipboard
Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov
| Challenge: | Existing methods for text classification tasks are inherently ambiguous and can cause errors. |
| Approach: | They propose a method that combines epistemic and aleatoric uncertainty to estimate toxicity detection errors. |
| Outcome: | The proposed method outperforms existing methods for toxicity detection and other ambiguous text classification tasks. |
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting (2023.acl-long)
Copied to clipboard
Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, Alham Fikri Aji, David Ifeoluwa Adelani, Khalid Almubarak, M Saiful Bari, Lintang Sutawika, Jungo Kasai, Ahmed Baruwa, Genta Winata, Stella Biderman, Edward Raff, Dragomir Radev, Vassilina Nikoulina
| Challenge: | Existing language adaptation strategies for multilingual models are limited to 46 languages . a new language is added to the model to improve zero-shot prompting performance . |
| Approach: | They apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. |
| Outcome: | The proposed model can be extended to other languages without incurring prohibitively large costs. |
Logic-driven Indirect Supervision: An Application to Crisis Counseling (2023.acl-long)
Copied to clipboard
Mattia Medina Grespan, Meghan Broadbent, Xinyao Zhang, Katherine Axford, Brent Kious, Zac Imel, Vivek Srikumar
| Challenge: | Text-based crisis counseling services are increasingly adopted by people seeking confidential mental health support. |
| Approach: | They propose an inexpensive method that exploits declaratively stated structural dependencies between both levels of annotation to improve utterance modeling. |
| Outcome: | The proposed method improves utterance modeling by 3.5% over a strong multitask baseline. |
Grounding Characters and Places in Narrative Text (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. |
| Approach: | They propose a task to assign a spatial relationship category for every character and location co-mention within a window of text, taking into account linguistic context, narrative tense, and temporal scope. |
| Outcome: | The proposed model allows to test hypotheses on mobility and domestic space . women as characters tend to occupy more interior space than men, the model shows . |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
Copied to clipboard
| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT (2023.acl-long)
Copied to clipboard
| Challenge: | NLP literature has not given enough attention to the phenomenon of negative transfer . positive transfer refers to the facilitating effects of one language in acquiring another and negative transfer refer to the negative effects between the learner's native [L1] and target [L2] languages. |
| Approach: | They build a Mutlilingual Age Ordered CHILDES dataset to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
| Outcome: | The proposed model enables us to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for classification are overly confident on unseen examples . despite recent advances in NLP, some categories of distribution shift still pose serious challenges. |
| Approach: | They propose a method that generates OOD examples representative of novel classes and trains to decrease confidence on them. |
| Outcome: | The proposed method improves classifiers' ability to detect and abstain on novel class examples over previous methods by 2.3% and 5.5% over previous approaches. |
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning? (2023.acl-long)
Copied to clipboard
| Challenge: | Prompt tuning (PT) based on frozen pre-trained language models has shown remarkable performance in few-shot learning . however, it relies heavily on good initialization of the prompt embeddings. |
| Approach: | They propose to use meta prompt tuning to improve cross-task generalization by learning to initialize prompt embeddings from other relevant tasks. |
| Outcome: | The proposed method outperforms PT on classification tasks, but not multi-task learning. |
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale (2023.acl-long)
Copied to clipboard
| Challenge: | 70% of attention heads and 20% of the feed forward networks can be removed with minimal decline in task performance. |
| Approach: | They propose to investigate whether in-context learning is not uniform across all components of a large language model. |
| Outcome: | The proposed model can remove 70% of attention heads and 20% of feed forward networks with minimal decline in task performance. |
Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya (2023.acl-long)
Copied to clipboard
| Challenge: | Question-Answering (QA) has seen significant advances in recent years, achieving near human-level performance over some benchmarks. |
| Approach: | They propose to use a native QA dataset for an East African language, Tigrinya, to build similar resources for related languages. |
| Outcome: | The proposed method is applicable to constructing similar resources for related languages. |
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain (2023.acl-long)
Copied to clipboard
| Challenge: | Increasing number of NLP benchmarks highlight need for multilingual models for job-related tasks. |
| Approach: | They introduce a language model called ESCOXLM-R that uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations taxonomy. |
| Outcome: | The proposed model outperforms XLM-R-large on short spans and entity-level and surface-level span-F1 tasks on entity- and surface level. |
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval (2023.acl-long)
Copied to clipboard
Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing multi-vector retrieval methods are slower and require more space to store indices compared to their single-vektor counterparts. |
| Approach: | They propose a multi-vector retrieval method that uses dynamic lexical routing to route different token vectors to the predicted lexicals. |
| Outcome: | The proposed method achieves state-of-the-art performance on several benchmark datasets while being nearly 40 times faster than the current state-out-of the-art method. |
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for supervised visual captioning require large scale of images or videos paired with descriptions in a specific language. |
| Approach: | They propose a zero-shot approach that generates captions for different scenarios without labeling . they use concept prompts to retrieve concepts and auto-encode them to learn writing styles . |
| Outcome: | The proposed approach generates captions for different scenarios and languages without labeled vision-caption pairs. |
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge (2023.acl-long)
Copied to clipboard
Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, H. Andrew Schwartz
| Challenge: | Active learning has been proposed to alleviate data acquisition challenges for rare-class tasks when the class label is very infrequent (e.g., 5% of samples). |
| Approach: | They propose to use transformers to train models on closely related tasks and evaluate acquisition strategies, including a proposed probability-of-rare-class approach to dissonance detection. |
| Outcome: | The proposed method improves model accuracy while iterative transfer-learning does not improve cold-start performance. |
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on curriculum learning rely on task-specific expertise and cannot generalize to different tasks. |
| Approach: | They propose to do in-sample curriculum learning for natural language generation tasks using human-crafted rules and a numeric score for each sample based on domain expertise to rank the model. |
| Outcome: | The proposed learning strategy generalizes well to different tasks and achieves significant improvements over baselines. |
Product Question Answering in E-Commerce: A Survey (2023.acl-long)
Copied to clipboard
| Challenge: | Product question answering (PQA) aims to automatically provide instant responses to customer’s questions in E-commerce platforms. |
| Approach: | They categorize PQA studies into four problem settings in terms of the form of provided answers. |
| Outcome: | The proposed methods capture the unique challenges of product question answering (PQA) . |
Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language (2023.acl-long)
Copied to clipboard
| Challenge: | Domain adaptation (DA) techniques have been used to improve performance of NLP systems for healthcare tasks due to numerous complexities of data. |
| Approach: | They propose to use domain adaptation techniques to improve generalizability across diverse datasets for dementia detection. |
| Outcome: | The proposed model achieves a 22% increase in accuracy adapting from a conversational to task-oriented dataset compared to a jointly trained baseline. |
Generalizing Backpropagation for Gradient-Based Interpretability (2023.acl-long)
Copied to clipboard
| Challenge: | Several feature-attribution methods for interpreting deep neural networks rely on computing the gradients of a model’s output with respect to its inputs, but they reveal little about the inner workings of the model itself. |
| Approach: | They propose a generalized backpropagation algorithm that generalizes the gradient computation of a model to efficiently compute other interpretable statistics about the gradient graph of neural networks. |
| Outcome: | The proposed generalized algorithm can be used to compute other interpretable statistics about the gradient graph of a neural network, such as the highest-weighted path and entropy. |
UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have incorporated contextual information to better learn the representation of political actors for specific tasks. |
| Approach: | They propose to use statements to represent political actors and learn mapping from languages to representations using social networks and behaviors as self-constructed supervision. |
| Outcome: | The proposed model can be generalized to political actors and solve downstream tasks. |
Generic Temporal Reasoning with Differential Analysis and Explanation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing temporal reasoning models drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions. |
| Approach: | They propose a task called TODAY that evaluates whether systems can correctly understand the effect of incremental changes in temporal relation distributions. |
| Outcome: | The proposed task outperforms existing models, including GPT-3.5, on in-domain benchmarks while allowing for more appropriate annotations. |
Model-Based Simulation for Optimising Smart Reply (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to learn to predict responses to messages are based on post-hoc diversification rather than learning to predict sets of responses. |
| Approach: | They propose a method that employs model-based simulation to discover high-value response sets by simulating possible user responses with a learned world model. |
| Outcome: | Empirically, the proposed method improves ROUGE score and Self-ROUGE scores on two public datasets compared to baselines. |
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval (2023.acl-long)
Copied to clipboard
| Challenge: | Contrastive learning is the dominant paradigm for learning text representations from parallel text, but finding negative examples can be expensive in terms of compute or manual effort. |
| Approach: | They propose a generative model for learning multilingual text embeddings which encourages source separation in multilingual contexts by an approximation. |
| Outcome: | The proposed model outperforms both a strong contrastive and generative baseline on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval. |
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text generation evaluation metrics are lacking in robustness analysis. |
| Approach: | They propose to use stress tests to test for errors in text generation evaluation metrics . they find that BERTScore is confused by truncation errors in summarization . |
| Outcome: | The proposed stress tests show that they are insensitive to errors in open-ended generation, translation, and summarization. |
Dealing with Semantic Underspecification in Multimodal NLP (2023.acl-long)
Copied to clipboard
| Challenge: | a linguistic signal can leave underspecified information, such as gender and number . this problem is a crucial feature that boosts language's storage and processing efficiency . |
| Approach: | They argue that intelligent systems must deal with semantic underspecification . it is a feature that boosts language's storage and processing efficiency, they argue . they argue that the problem is not a bug but a problem that could negatively affect performance . |
| Outcome: | a new paper shows that systems that aim at mastering language must deal with semantic underspecification . it shows that human speakers can integrate semantically-underspecified linguistic signals with non-linguistic information . |
Trigger Warning Assignment as a Multi-Label Document Classification Problem (2023.acl-long)
Copied to clipboard
| Challenge: | a trigger warning is used to warn people about potentially disturbing content . a webis dataset of 1 million fanfiction works contains up to 36 different warnings per document . |
| Approach: | They introduce a multi-label task to assign a trigger warning to fanfiction . they map 41 million free-form tags assigned by authors into a single taxonomy of trigger warnings . |
| Outcome: | The proposed model achieves micro-F1 scores of about 0.5, which reveals the difficulty of the task. |
WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings (2023.acl-long)
Copied to clipboard
| Challenge: | Extensive experiments on seven semantic textual similarity tasks show our method achieves consistent improvement over the contrastive learning baseline and sets new states of the art. |
| Approach: | They propose a whitening-based contrastive learning method for sentence embedding learning which combines contrastive and shuffled group whitening. |
| Outcome: | The proposed method achieves better alignment and uniformity on seven semantic textual similarity tasks. |
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms (2023.acl-long)
Copied to clipboard
| Challenge: | Neural semantic parsers have achieved remarkable performance in recent years, but they are data-hungry and require annotators to have intimate knowledge of formal programs. |
| Approach: | They propose a task where multiple clients collaboratively train one global model without sharing their semantic parsing data. |
| Outcome: | The proposed model improves performance on three widely adopted FL algorithms (FedAvg, FedOPT and FedProx) and clients with smaller datasets enjoy faster performance. |
Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance. |
| Approach: | They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets. |
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing adversarial training methods require multi-step gradient ascents or word substitutions to obtain adversarials, which impairs the effectiveness of adversariarial training. |
| Approach: | They propose a procedure for instead adversarial training with only clean data that estimates the adversarials loss by perturbing the input data’s probability distribution rather than their embeddings. |
| Outcome: | The proposed procedure reduces time consumption by up to 70% compared to current best-performing adversarial training methods. |
A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to detecting mental disorders lack domain-based interpretation . lack of quality data or complexity of models can cause problems . |
| Approach: | They propose a model that captures semantic meanings directly from social media and compares them to symptom-related descriptions. |
| Outcome: | The proposed model outperforms baselines on mental disorder detection tasks. |
Downstream Datasets Make Surprisingly Good Pretraining Corpora (2023.acl-long)
Copied to clipboard
| Challenge: | a dominant practice is to fine tune large pretrained transformer models using smaller downstream datasets . performance gains are not always attributable to the use of external data in massive amounts . |
| Approach: | They propose to use the same (downstream) training data for pretraining and finetuning to compare models. |
| Outcome: | The proposed model outperforms standard pretraining on the BookWiki corpus on 7 and 5 datasets. |
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Existing task setting for attribute mining on e-commerce products is closed-world, but recent work has moved towards open-world aspect. |
| Approach: | They propose a task setting for attribute mining on e-commerce products that uses a high-quality seed attribute set bootstrapped from existing resources. |
| Outcome: | The proposed approach surpasses baselines on existing attributes by 12 F1 and discovers values from 39% new attributes. |
XDailyDialog: A Multilingual Parallel Dialogue Corpus (2023.acl-long)
Copied to clipboard
Zeming Liu, Ping Nie, Jie Cai, Haifeng Wang, Zheng-Yu Niu, Peng Zhang, Mrinmaya Sachan, Kaiping Peng
| Challenge: | Existing datasets for open-domain dialogue modeling limited to a single language . absence of multilingual datasets hinders development of robust open- domain dialog systems . |
| Approach: | They propose a multilingual parallel open-domain dialog dataset to explore multilingual and cross-lingual open- domain dialog. |
| Outcome: | The proposed model can be used to explore multilingual and cross-lingual open-domain dialogs in other languages. |
PAL to Lend a Helping Hand: Towards Building an Emotion Adaptive Polite and Empathetic Counseling Conversational Agent (2023.acl-long)
Copied to clipboard
| Challenge: | The social stigma associated with mental illness prevents individuals from addressing their issues and getting assistance. |
| Approach: | They propose to build a Polite and empAthetic conversational agent PAL to lay down the counseling support to substance addicts and crime victims. |
| Outcome: | The proposed agent is scalable and can be easily modified with different modules of preference models as per need. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
| Outcome: | The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features. |
Contrastive Decoding: Open-ended Text Generation as Optimization (2023.acl-long)
Copied to clipboard
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis
| Challenge: | Using a language model, maximum probability is a poor decoding objective because it produces short and repetitive text. |
| Approach: | They propose a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. |
| Outcome: | The proposed approach outperforms four strong decoding algorithms in automatic and human evaluations across wikipedia, news and story domains. |
Resolving Indirect Referring Expressions for Entity Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in language modeling have enabled new conversational systems. |
| Approach: | They propose to use a dataset of indirect referring expressions to solve the problem of reference resolution when people use natural expressions . they propose to model the problem using 42K indirect referred expressions across three domains and a public dataset of entity pairs and utterances. |
| Outcome: | The proposed models achieve 82%-87% accuracy in realistic settings, while reasonable invites further advances. |
Accelerating Transformer Inference for Translation via Parallel Decoding (2023.acl-long)
Copied to clipboard
Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodola
| Challenge: | Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT) Existing methods to solve this problem are expensive and require changes to the model. |
| Approach: | They propose to reframe autoregressive decoding with a parallel formulation . they propose to speed up existing models without training or modifications while retaining translation quality. |
| Outcome: | The proposed model speeds up existing models without training or modifications while retaining translation quality. |
Hard Sample Aware Prompt-Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Prompt-tuning based few-shot learning has garnered increasing attention in recent years due to its efficiency and promising capability. |
| Approach: | They propose a framework to distinguish informative hard samples from misleading ones in model training. |
| Outcome: | The proposed framework achieves new SOTA results on a series of NLP tasks pushing the SST-5 accuracy to 49.5% (1.1% point absolute improvement), QNLI accuracy to 74.6% (1.9% absolute improvement) |
WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events (2023.acl-long)
Copied to clipboard
Marco Antonio Stranisci, Rossana Damiano, Enrico Mensa, Viviana Patti, Daniele Radicioni, Tommaso Caselli
| Challenge: | Existing corpora and models for biographical event detection are lacking . Detecting biographical events from unstructured data is a useful task to explore and compare bias in representations of individuals. |
| Approach: | They present a corpus annotated for biographical event detection using 20 Wikipedia biographies and 5 existing corpora to train a model. |
| Outcome: | The proposed model detects all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an 0.859 score. |
Best-k Search Algorithm for Neural Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. |
| Approach: | They propose a deterministic search algorithm balancing quality and diversity . they investigate the vanilla best-first search algorithm and propose k-k search algorithm. |
| Outcome: | The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
Copied to clipboard
Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |
Transforming Visual Scene Graphs to Image Captions (2023.acl-long)
Copied to clipboard
Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
| Approach: | They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words. |
| Outcome: | The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA . |
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023.acl-long)
Copied to clipboard
| Challenge: | Neural based end-to-end frameworks have achieved remarkable success in speech-totext tasks, such as automatic speech recognition (ASR) and speech- totext translation (ST). |
| Approach: | They propose to combine Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks and leverage AED's strength in non-monotonic sequence to sequence learning while retaining Transducers streaming property. |
| Outcome: | The proposed model outperforms Transducer and Attention based Encoder-Decoder (TAED) on the MuST-C dataset and shows that it is not bound by any specific language model. |
Improving Domain Generalization for Prompt-Aware Essay Scoring via Disentangled Representation Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing AES models are either prompt-specific or prompt-adaptive and cannot generalize well on “unseen” prompts. |
| Approach: | They propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. |
| Outcome: | The proposed model extracts comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. |
What’s the Meaning of Superhuman Performance in Today’s NLU? (2023.acl-long)
Copied to clipboard
Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajič, Daniel Hershcovich, Eduard Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli
| Challenge: | Recent research has focused on developing larger pretrained language models and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities. |
| Approach: | They propose to use benchmarks such as SuperGLUE and SQUAD to evaluate PLMs' abilities in language understanding, reasoning, and reading comprehension to assess their performance. |
| Outcome: | The proposed benchmarks have serious limitations affecting comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks. |
PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
Copied to clipboard
Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
| Approach: | They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities. |
| Outcome: | The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism . |
Hints on the data for language modeling of synthetic languages with transformers (2023.acl-long)
Copied to clipboard
| Challenge: | Language Models (LMs) are becoming more useful for providing representations for NLP applications. |
| Approach: | They evaluated whether the critical amount of data varies for different morphological typologies . they found that the size of the vocabulary due to morphology is directly correlated with LM perplexity . |
| Outcome: | The proposed method reduces perplexity by more than a half for a polysynthetic language like Quechua . |
Neural Machine Translation Methods for Translating Text to Sign Language Glosses (2023.acl-long)
Copied to clipboard
| Challenge: | State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language glosses. |
| Approach: | They propose to use data augmentation, semi-supervised Neural Machine Translation, transfer learning and multilingual NMT to improve MT of spoken language to Sign Language glosses. |
| Outcome: | The proposed models outperform previous work on two German SL corpora and are confirmed by human evaluation. |
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies on event argument extraction (EAE) have not taken event co-occurrences into account. |
| Approach: | They propose to reformulate event co-occurrences as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework that extracts the arguments of multiple events in parallel. |
| Outcome: | The proposed framework can extract arguments of multiple events in parallel. |
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation. |
| Approach: | They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion . |
| Outcome: | The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. |
Large-scale Lifelong Learning of In-context Instructions and How to Tackle It (2023.acl-long)
Copied to clipboard
| Challenge: | In-context instruction learning is a method to improve the target PLM’s instance- and task-level generalization performance as it observes more tasks. |
| Approach: | They propose to fine-tune a Pre-trained Language Model (PLM) on a set of tasks with in-context instructions and to extend this property to a scenario in which tasks are fed to the target PLM in a sequential manner. |
| Outcome: | The proposed method achieves noticeable improvements in both types of generalization, nearly reaching the upper bound performance obtained through joint training. |
Controllable Text Generation via Probability Density Estimation in the Latent Space (2023.acl-long)
Copied to clipboard
| Challenge: | Existing control approaches cannot effectively model complex space with diverse attributes, high dimensionality, and asymmetric structure, leaving subsequent controls unsatisfactory. |
| Approach: | They propose a control framework using probability density estimation in the latent space and an invertible transformation function that maps the complex distributions to simple Gaussian distributions in the prior space. |
| Outcome: | The proposed method outperforms baselines on attribute relevance and text quality, achieving a new SOTA. |
Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times. |
| Approach: | They propose a Latent relations Learning method for TKG reasoning that captures latent relations between entities at different times. |
| Outcome: | The proposed method exploits the intra- and inter-time latent relations of entities at different times. |
DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)
Copied to clipboard
Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang
| Challenge: | Recent advances in neural theorem-proving resort to large language models and tree searches. |
| Approach: | They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate. |
Unsupervised Selective Rationalization with Noise Injection (2023.acl-long)
Copied to clipboard
| Challenge: | Unsupervised selective rationalization produces rationales alongside predictions, but does not ensure that the rationale contains a plausible explanation for the prediction. |
| Approach: | They propose a technique that injects noise between a rationale generator and a predictor to limit generation of implausible rationales. |
| Outcome: | The proposed method achieves significant improvements in plausibility and task accuracy over the state-of-the-art models while maintaining or improving model faithfulness. |
Understanding In-Context Learning via Supportive Pretraining Data (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a form of learning that provides a handful of examples at inference time, but it is not well understood why it emerges as the model has never been specifically trained on such demonstrations. |
| Approach: | They adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL and compare it with random subsets of pretrain data. |
| Outcome: | The proposed method improves the model's ICL ability by 18% if it is continued on a small subset of pretraining data. |
ETHICIST: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained language models memorize a considerable fraction of training data, leading to privacy risk of information leakage. |
| Approach: | They propose a method for targeted training data extraction using a smoothed soft prompting and calibrated confidence estimation. |
| Outcome: | The proposed method significantly improves the extraction performance on a recently proposed public benchmark. |
Effective Contrastive Weighting for Dense Query Expansion (2023.acl-long)
Copied to clipboard
| Challenge: | Verbatim queries that do not adequately express the user's search intent are often lexical inadequacies. |
| Approach: | They propose a contrastive weighting model that learns to select the most useful expansion embeddings for semantic search. |
| Outcome: | The proposed model outperforms existing methods while maintaining its efficiency. |
Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore (2023.acl-long)
Copied to clipboard
Janosch Haber, Bertie Vidgen, Matthew Chapman, Vibhor Agarwal, Roy Ka-Wei Lee, Yong Keong Yap, Paul Röttger
| Challenge: | Toxic content is a global problem, but most resources for detecting toxic content are in English . new datasets and models for non-English languages focus exclusively on one language or dialect . |
| Approach: | They propose to use a multilingual dataset of online attacks to identify code-mixed toxic content in Singapore . they collect reddit comments in Indonesian, Malay, Singlish, and other languages and provide fine-grained hierarchical labels for attacks . |
| Outcome: | The proposed dataset provides fine-grained hierarchical labels for online attacks in Singapore . it shows that the metadata can be used for granular error analysis . |
Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | We use both Bayesian and neural models to dissect a data set of Chinese learners’ pre- and post-interventional responses to two tests measuring their understanding of English prepositions. |
| Approach: | They use Bayesian and neural models to dissect Chinese learners' responses to two tests measuring their understanding of English prepositions. |
| Outcome: | The proposed model can predict grammaticality and learnability based on language model probabilities. |
Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to control document controllable summarization lack abundant labeled data. |
| Approach: | They propose a question-driven, unsupervised pretraining objective to improve controllability in document controllable summarization tasks. |
| Outcome: | The proposed method outperforms pre-finetuning approaches on QMSum and SQuALITY. |
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering (2023.acl-long)
Copied to clipboard
Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Eisenschlos
| Challenge: | Visual language models that are pretraining on natural images or image-text pairs crawled from the web perform poorly on visual language tasks such as ChartQA and ChartQA. |
| Approach: | They propose to perform several pretraining tasks that cover plot deconstruction and numerical reasoning which are key capabilities in visual language modeling. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmarks such as PlotQA and ChartQA by as much as 20%. |
MGR: Multi-generator Based Rationalization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to explain NLP models have two key challenges: spurious correlation and degeneration. |
| Approach: | They propose a rationalization framework using a generator and a predictor to construct a self-explaining NLP model with spurious correlation and degeneration as key challenges. |
| Outcome: | The proposed method improves the F1 score by 20.9% compared to state-of-the-art methods. |
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics (2023.acl-long)
Copied to clipboard
Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, but they are insufficient for diagnosing whether metrics are consistent, effective on human-written texts, and sensitive to different error types. |
| Approach: | They propose to use unfaithful minimal pairs to measure the consistency of automatic faithfulness metrics by comparing human-written summary pairs with a dataset of 889 human-writing, minimally different summary pairs. |
| Outcome: | The proposed benchmarks show that the most discriminative metrics tend not to be the most consistent, and that the best performing metrics are sensitive to errors. |
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Out-of-distribution (OOD) detection is critical for reliable predictions over text . fine-tuning with pre-trained language models has been a de facto procedure . |
| Approach: | They propose to leverage pre-trained language models for OOD detection without fine-tuning on ID data. |
| Outcome: | The proposed approach outperforms the fine-tuned model under distributional shifts. |
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets. |
| Approach: | They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task . |
| Outcome: | The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation. |
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Evaluating open-domain dialogue systems is challenging because of the one-to-many problem. |
| Approach: | They propose a reference-based dialogue evaluation approach that leverages the pre-created utterance as reference other than the gold response to relieve the one-to-many problem. |
| Outcome: | The proposed method outperforms state-of-the-art evaluation methods on three datasets and two existing benchmarks. |
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
PuMer: Pruning and Merging Tokens for Efficient Vision Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Large-scale vision language models use Transformers to perform cross-modal interactions . state-of-the-art models are memory intensive and expensive due to quadratic complexity . |
| Approach: | They propose a token reduction framework that uses text-informed Pruning and modality-aware Merging strategies to progressively reduce the tokens of input image and text. |
| Outcome: | The proposed framework improves inference speed and memory footprint on four vision language tasks. |
Gloss-Free End-to-End Sign Language Translation (2023.acl-long)
Copied to clipboard
| Challenge: | a study of sign language translation without gloss annotations focuses on the problem of gloss annotation . gloss annotation is hard to acquire, especially in large quantities, and limits the domain coverage of translation datasets . |
| Approach: | They propose a gloss-free end-to-end sign language translation framework to solve this problem . gloss annotations are hard to acquire, especially in large quantities, they argue . |
| Outcome: | The proposed framework improves sign language translation performance on large-scale datasets . gloss annotations are hard to acquire, especially in large quantities . |
TAGPRIME: A Unified Framework for Relational Structure Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for natural language processing (NLP) do not address common tasks. |
| Approach: | They propose to take a unified view of all the tasks and introduce a model that appends priming words about the condition to the input text. |
| Outcome: | The proposed model is based on ten datasets across five different languages and covers ten tasks that cover ten languages. |
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers (2023.acl-long)
Copied to clipboard
Linyuan Gong, Chenyan Xiong, Xiaodong Liu, Payal Bajaj, Yiqing Xie, Alvin Cheung, Jianfeng Gao, Xia Song
| Challenge: | Recent work in NLP has shown that pretrained language models have made notable progress toward generalization to unseen tasks. |
| Approach: | They propose to pretrain T5 using an auxiliary model to construct more challenging token replacements for the main model to denoise. |
| Outcome: | The proposed model outperforms similar-sized baseline models on prompted NLP benchmarks and rivals the state-of-the-art model with only **8%** of its parameters. |
BITE: Textual Backdoor Attacks with Iterative Trigger Injection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to defend against backdoor attacks are based on model stealing, model thieving and training data extraction attacks. |
| Approach: | They propose a backdoor attack that poisons training data to establish strong correlations between the target label and a set of “trigger words” These trigger words are iteratively identified and injected into the target-label instances through natural word-level perturbations. |
| Outcome: | The proposed attack is significantly more effective than baseline methods while maintaining decent stealthiness, raising alarm on the usage of untrusted training data. |
A Crosslingual Investigation of Conceptualization in 1335 Languages (2023.acl-long)
Copied to clipboard
Yihong Liu, Haotian Ye, Leonie Weissweiler, Philipp Wicke, Renhao Pei, Robert Zangenfeind, Hinrich Schütze
| Challenge: | Conceptualizer is a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings. |
| Approach: | They propose a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings. |
| Outcome: | The proposed method has good alignment accuracy across all languages and on 32 Swadesh concepts. |
Exploring and Verbalizing Academic Ideas by Concept Co-occurrence (2023.acl-long)
Copied to clipboard
| Challenge: | a new framework for academic idea inspiration is being developed for academic research assistants . number of academic publications is increasing exponentially, making it difficult for an independent researcher to understand these papers thoroughly. |
| Approach: | They propose a framework based on concept co-occurrence for academic idea inspiration . they construct evolving concept graphs according to the co-existence relationship of concepts from 20 disciplines or topics . |
| Outcome: | The proposed system can be used to explore connections between academic concepts and verbalize the new ideas. |
mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)
Copied to clipboard
Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang
| Challenge: | Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs. |
| Approach: | They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method. |
| Outcome: | Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
Extrinsic Evaluation of Machine Translation Metrics (2023.acl-long)
Copied to clipboard
| Challenge: | MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets. |
| Approach: | They evaluate the segment-level performance of the most widely used MT metrics by correlating them with how useful they are for downstream tasks. |
| Outcome: | The MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets. |
ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for meeting summarization use extract-thengenerate method to select "salient" contents . extract-thangenerates method typically selects "selected" content in a distantly supervised manner . |
| Approach: | They propose a novel extractor-guided method to generate a summary from evidence sentences that "explain" a meeting summary. |
| Outcome: | The proposed method outperforms existing methods with gains of up to 3.13 in the ROUGE-1 score. |
A Cross-Modality Context Fusion and Semantic Refinement Network for Emotion Recognition in Conversation (2023.acl-long)
Copied to clipboard
| Challenge: | Emotion recognition in conversation studies focus on textual modality, but they lack contextual information and focus on a limited number of modalities. |
| Approach: | They propose a cross-modal context fusion and semantic refinement network to explore multimodal interactions and a graph-based semantic refinements transformer to solve the limitation of insufficient semantic relationship information between utterances. |
| Outcome: | The proposed method is compared with other state-of-the-art methods on two public benchmark datasets and shows its potential for emotion recognition. |
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | HKUST-KnowComp proposes a framework for commonsense reasoning that can be used to conceptualize commonsence knowledge bases at scale. |
| Approach: | They propose a framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale. |
| Outcome: | The proposed framework achieves state-of-the-art on two conceptualization tasks and the acquired abstract commonsense knowledge significantly improves commonsence inference modeling. |
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research (2023.acl-long)
Copied to clipboard
Mohamed Abdalla, Jan Philip Wahle, Terry Ruas, Aurélie Névéol, Fanny Ducel, Saif Mohammad, Karen Fort
| Challenge: | Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. |
| Approach: | They examine industry presence in the field since the early 90s and characterize it using a corpus of 78,187 NLP publications and 701 resumes of NLP publication authors. |
| Outcome: | The authors find that industry presence among NLP authors has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). |
Language of Bargaining (2023.acl-long)
Copied to clipboard
| Challenge: | a new dataset is being developed to study how language shapes bilateral bargaining . a recent study examined the use of language in negotiation education . |
| Approach: | They propose a dataset to study how language shapes bilateral bargaining . they recruit participants via behavioral labs instead of crowdsourcing platforms . |
| Outcome: | The proposed dataset is based on an exercise in negotiation education . it shows that when subjects can talk, negotiations finish faster and prices drop . |
Do Question Answering Modeling Improvements Hold Across Benchmarks? (2023.acl-long)
Copied to clipboard
| Challenge: | a new study finds that human-constructed and downsampled benchmarks hold more concurrence than downsampled benchmarks. |
| Approach: | They propose to measure concurrence between two QA benchmarks on a set of 20 models . they find that human-constructed benchmarks have high concurrence amongst themselves . |
| Outcome: | The proposed models hold broadly across the diverse landscape of question answering (QA) benchmarks. |
VLN-Trans: Translator for the Vision and Language Navigation Agent (2023.acl-long)
Copied to clipboard
| Challenge: | We observe two kinds of instructions that make the grounding in the vision-and-language navigation task quite challenging. |
| Approach: | They propose to use a translator module to convert instructions into easy-to-follow sub-instruction representations at each step. |
| Outcome: | The proposed model is based on a Room2Room (R2R), Room4room (R4R), and Room2room Last (R1R-Last) datasets and achieves state-of-the-art results on multiple benchmarks. |
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge distillation methods require access to internal information of teachers . however, such information is not always accessible for large pre-trained language models . |
| Approach: | They propose a method to estimate logits from the decision distributions using logits theoretically and empirically. |
| Outcome: | The proposed method outperforms baselines on natural language understanding and machine reading comprehension datasets. |
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) has been challenging in low-resource domains and with limited resources. |
| Approach: | They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. |
| Outcome: | The proposed method outperforms PLM-based RE classifier on two document-level RE datasets. |
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning . MU can be used to forget specific training instances as if they have never existed . |
| Approach: | They propose a general unlearning framework called KGA to induce forgetfulness . they propose several unlearning evaluation metrics with pertinence . |
| Outcome: | The proposed framework improves on large-scale datasets and provides insight into unlearning for NLP tasks. |
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on decoding word-level fMRI volumes from a restricted vocabulary. |
| Approach: | They propose an open-vocabulary task to bridge fMRI time series and human language . they use a pre-trained language model to construct a robust encoder for cognitive signals . |
| Outcome: | The proposed task bridges fMRI time series and human language with a baseline model. |
Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths (2023.acl-long)
Copied to clipboard
| Challenge: | ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., head event, relation, tail event. |
| Approach: | They propose a CSKG completion method called Rel-CSKGC to predict the relation given the head event and tail event of a triplet and train a model based on existing triplets. |
| Outcome: | The proposed method is based on existing triplets and can be used to complete the missing links in ATOMIC. |
Shrinking Embeddings for Hyper-Relational Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on binary relational KGs where each fact is represented by a triple. |
| Approach: | They propose a geometric hyper-relational KG embedding method that explicitly models qualifier monotonicity, qualifier implication, and qualifier mutual exclusion. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks of hyper-relational KGs. |
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
Copied to clipboard
Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
Attention as a Guide for Simultaneous Speech Translation (2023.acl-long)
Copied to clipboard
| Challenge: | EDAtt uses attention patterns to determine when to emit partial translations . results show that it yields better results compared to existing SimulST policies . |
| Approach: | They propose an adaptive policy that exploits attention patterns between audio source and target textual translation to guide an offline-trained ST model during simultaneous inference. |
| Outcome: | The proposed policy yields better results on en->de, compared to the current state of the art. |
On Complementarity Objectives for Hybrid Retrieval (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to hybrid retrieval focus on sparse models to capture “residual” features neglected in spars. |
| Approach: | They propose a new objective to capture a fuller notion of complementarity . they propose to improve the model's Ratio of Complementarity to improve RoC . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative IR benchmarks with statistical significance. |
C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in zero-shot stance detection are limited to English and Chinese . stance can provide useful information for important events such as policymaking and presidential elections. |
| Approach: | They present a Chinese dataset for zero-shot stance detection that is the first for ZSSD. |
| Outcome: | The proposed dataset is the first Chinese dataset for zero-shot stance detection. |
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)
Copied to clipboard
Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu
| Challenge: | Existing solutions for visual document understanding lack granularity of document textlines. |
| Approach: | They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts. |
| Outcome: | The proposed system performs better on various VDU tasks in English and Chinese. |
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on multi-modal dialogue pre-training is limited due to limited availability of multi-dimensional data . a recent emergence of chatGPT 1 has increased confidence in the potential for this goal . |
| Approach: | They propose a framework for multi-modal dialogue pre-training that integrates experts to accommodate multi-faceted tasks. |
| Outcome: | The proposed framework achieves state-of-the-art on eight multi-modal dialog benchmarks. |
MVP-Tuning: Multi-View Knowledge Retrieval with Prompt Tuning for Commonsense Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for commonsense reasoning rely on multi-hop knowledge retrieval and suffer low accuracy due toembedded noise in the acquired knowledge. |
| Approach: | They propose to use multi-hop knowledge retrieval to model knowledge and input text together. |
| Outcome: | The proposed method outperforms baselines on 5 commonsense reasoning datasets and is number one on theleaderboard. |
PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Image translation is a task that translates an image containing text in the source language to the target language. |
| Approach: | They propose an end-to-end image translation framework that bridges the modality gap between visual inputs and textual inputs/outputs of machine translation (MT). |
| Outcome: | The proposed framework outperforms existing models on a large-scale image translation corpus . it significantly outperformed both cascaded and strong models on the e-commerce domain . |
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Stance Detection is a task that aims to identify the attitudes of an author towards a target of interest. |
| Approach: | They propose a topic-guided diversity sampling technique and a contrastive objective to improve stance detection using the produced set. |
| Outcome: | The proposed method outperforms the state-of-the-art on 16 datasets with in-domain and out-of domain evaluations and is more generalizable with an averaged 10.2 F1 on out-domain evaluation. |
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles (2023.acl-long)
Copied to clipboard
| Challenge: | Advanced knowledge of a science or engineering domain is typically found in domain-specific research papers. |
| Approach: | They propose a task of extracting compositions of materials from tables in materials science papers to facilitate research in this direction. |
| Outcome: | The proposed model outperforms previous table processing architectures by significant margins. |
Self-Instruct: Aligning Language Models with Self-Generated Instructions (2023.acl-long)
Copied to clipboard
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
| Challenge: | Large “instruction-tuned” language models depend heavily on human-written instruction data . this limited quantity, diversity, and creativity hinders the generality of the tuned model . |
| Approach: | They propose a framework for improving instruction-following capabilities of pretrained language models by bootstrapping off their own generations. |
| Outcome: | The proposed framework outperforms existing public instruction datasets by 5% . it generates instructions, input, and output samples, then filters invalid or similar ones . |
Disentangled Phonetic Representation for Chinese Spelling Correction (2023.acl-long)
Copied to clipboard
| Challenge: | Chinese Spelling Correction (CSC) aims to detect and correct erroneous characters in Chinese sentences. |
| Approach: | They propose to integrate phonetic and character representations to allow interaction between textual and phonetic information. |
| Outcome: | The proposed method is superior to other methods on three benchmarks. |
Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Length extrapolation allows training a transformer language model on short sequences that preserves perplexities when tested on substantially longer sequences. |
| Approach: | They propose a relative positional embedding design that uses longer than the training sequence to create sandwich. |
| Outcome: | The proposed model can extrapolate to L ex L tr much better than other models. |
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on social biases in language models have focused on only English. |
| Approach: | They propose to use a Chinese dataset for bias evaluation and mitigation of Chinese conversational language models. |
| Outcome: | The proposed dataset includes under-explored bias categories, such as ageism and appearance biases, which received less attention in previous studies. |
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback (2023.acl-long)
Copied to clipboard
| Challenge: | Frozen models trained to mimic static datasets can never improve their performance. |
| Approach: | They propose to use binary quality measurements and free-form text feedback to improve conversational skills in a conversational learning framework. |
| Outcome: | The proposed model improves on the DIRECTOR model, which is based on binary quality measurements and free-form text feedback, and shows that iterative retraining and redeployment can improve the model. |
Uncovering and Categorizing Social Biases in Text-to-SQL (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Text-to-SQL models are trained on clean, neutral datasets, such as Spider and WikiSQl, but these models contain social bias at different rates. |
| Approach: | They propose to use data to map natural language utterances to SQL queries. |
| Outcome: | The proposed model can contain social bias at different rates in the downstream Text-to-SQL task. |
On the Compositional Generalization in Versatile Open-domain Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multi-task learning suffer from interference among datasets or fail to effectively reuse knowledge and skills learned from other datasets. |
| Approach: | They propose a sparsely activated modular network with a well-rounded set of operators and instantiate each operator with an independent module. |
| Outcome: | The proposed model outperforms state-of-the-art supervised approaches on 4 datasets with only 10% training data thanks to the modular architecture and multi-task learning. |
What is the Real Intention behind this Question? Dataset Collection and Intention Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Using the Wikipedia discussions, we identified positive/neutral and negative intentions in questions . questions can also reflect implicit offenses such as highlighting one’s lack of knowledge or bolstering an alleged superior knowledge, which can lead to conflict in conversations. |
| Approach: | They propose to use a dataset to identify questions with positive/neutral and negative intentions and the underlying intention categories within each group to highlight tacit and apparent intents. |
| Outcome: | The proposed method highlights tacit and apparent intents and uses Transformers augmented by TF-IDF-based features to classify the main intention categories. |
Conjunct Resolution in the Face of Verbal Omissions (2023.acl-long)
Copied to clipboard
| Challenge: | Verbal omissions occur when verbs and arguments are omitted from subsequent clauses . state-of-the-art models struggle with this task, but have limited results . |
| Approach: | They propose a conjunct resolution task that uses a split-and-rephrase paradigm to recover verbal omissions . they propose omitted words in bold and omitted words in red . |
| Outcome: | The proposed method performs decently, but leaves ample room for improvement. |
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for cognitive behavioral therapy lack specific and diverse practice material. |
| Approach: | They propose to use a dataset to generate unhelpful thought patterns . they propose to train and evaluate existing models to generate an abundance of practice material . |
| Outcome: | The proposed model can generate unlimited quantity of practice material and generate suitable reframing proposals with no or minimal additional model training required. |
Learning In-context Learning for Named Entity Recognition (2023.acl-long)
Copied to clipboard
Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun
| Challenge: | Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations. |
| Approach: | They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. |
| Outcome: | The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors. |
Holistic Prediction on a Time-Evolving Attributed Graph (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for predicting future links, nodes, and attributes of time-evolving attributed graphs are not accurate. |
| Approach: | They propose a framework that predicts node attributes and topology changes such as appearance and disappearance of links and the emergence and loss of nodes. |
| Outcome: | The proposed framework improves on existing methods that assume that each link, node, and attribute prediction is independent and fails to predict new nodes that were not observed in the past. |
Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to joint Information Extraction (IE) neglect cross-instance or cross-task dependencies. |
| Approach: | They propose a joint IE framework that formulates joint 'conditional random field' to model cross-instance interactions . they incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method . |
| Outcome: | The proposed approach improves on three IE tasks compared with baseline and prior work. |
Training Trajectories of Language Models Across Scales (2023.acl-long)
Copied to clipboard
Mengzhou Xia, Mikel Artetxe, Chunting Zhou, Xi Victoria Lin, Ramakanth Pasunuru, Danqi Chen, Luke Zettlemoyer, Veselin Stoyanov
| Challenge: | Scaling up language models has led to unprecedented performance gains, but little is understood about how the training dynamics change as models get larger. |
| Approach: | They analyze the training checkpoints of different-sized OPT models on next-token prediction, sequence-level generation and downstream tasks. |
| Outcome: | The results show that language models of different sizes learn more during training . small models halt at hallucinations, larger ones learn to assign lower probabilities . |
A Diverse Set of Freely Available Linguistic Resources for Turkish (2023.acl-long)
Copied to clipboard
| Challenge: | despite the abundance of Turkish speakers, linguistic resources for natural language processing remain scarce. |
| Approach: | They propose a set of freely available linguistic resources for Turkish natural language processing . they provide corpora and pretrained models to help practitioners build their own applications . |
| Outcome: | The proposed linguistic resources are first of their kind and easy to use in a broad range of implementations. |
Measuring Consistency in Text-based Financial Forecasting Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in natural language processing (NLP) have allowed financial forecasting to gain significant accuracy and reliability. |
| Approach: | They propose a tool that assesses logical consistency in financial text and compares it with other models to assess their performance. |
| Outcome: | The proposed evaluation tool assesses logical consistency in financial text. |
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Neural machine translation models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. |
| Approach: | They propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. |
| Outcome: | The proposed detector outperforms existing models and is competitive with detectors that employ external models trained on millions of samples. |
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank (2023.acl-long)
Copied to clipboard
Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
| Challenge: | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing . contrastive learning methods fail to capture fine-grained ranking information among the sentences . |
| Approach: | They propose a novel approach for unsupervised sentence representation learning that integrates ranking consistency and ranking distillation with contrastive learning into a unified framework. |
| Outcome: | The proposed approach performs better over state-of-the-art models on STS and TR tasks. |
Entailment as Robust Self-Learner (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. |
| Approach: | They propose a prompting strategy that formulates different NLU tasks as contextual entailment and propose an algorithm for better pseudo-labeling quality in self-training. |
| Outcome: | The proposed approach improves the zero-shot adaptation performance on downstream tasks. |
ReCode: Robustness Evaluation of Code Generation Models (2023.acl-long)
Copied to clipboard
Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang
| Challenge: | Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area. |
| Approach: | They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. |
| Outcome: | The proposed model performs better on human annotators and on SOTA models with human annnotators. |
EPIC: Multi-Perspective Annotation of a Corpus of Irony (2023.acl-long)
Copied to clipboard
Simona Frenda, Alessandro Pedrani, Valerio Basile, Soda Marem Lo, Alessandra Teresa Cignarella, Raffaella Panizzon, Cristina Marco, Bianca Scarlini, Viviana Patti, Cristina Bosco, Davide Bernardi
| Challenge: | EPIC is the first annotated corpus for irony analysis based on data perspectivism . a recent trend in natural language processing (NLP) postulates that the disagreement among annotators in a language resource is a valuable source of knowledge, rather than noise that ought to be minimized or discarded. |
| Approach: | They propose to annotate an English perspectivist irony corpus based on data perspectivism . they validate the model by creating perspective-aware models that encode the perspectives of annotators grouped according to their demographic characteristics. |
| Outcome: | The proposed model can capture different perspectives on irony among different groups of annotators, and is more confident than non-perspectivist models. |
Dialogue Summarization with Static-Dynamic Structure Fusion Graph (2023.acl-long)
Copied to clipboard
| Challenge: | Dialogue summarization is a challenging task since it has dynamic interaction nature and inconsistent information flow among various speakers. |
| Approach: | They propose a Static-Dynamic graph-based Dialogue Summarization model which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion. |
| Outcome: | The proposed model can help people capture the highlights of a semi-structured and multi-participant dialogue without reviewing the complex dialogue context. |
Large-Scale Correlation Analysis of Automated Metrics for Topic Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on topic models lack a correlation between automated coherence metrics and human judgement. |
| Approach: | They propose a sampling approach to mine topics for metric evaluation and extend the analysis to measure topical differences between corpora. |
| Outcome: | The proposed method extends to measure topical differences between corpora and human judgement by using extensive user study. |
U-CREAT: Unsupervised Case Retrieval using Events extrAcTion (2023.acl-long)
Copied to clipboard
| Challenge: | Prior Case Retrieval (PCR) is about automatically citing relevant prior legal cases in a given query case. |
| Approach: | They propose a new benchmark for prior case retrieval based on a legal query case . they propose an unsupervised retrieval method-based pipeline U-CREAT . |
| Outcome: | The proposed method significantly improves performance and makes retrieval faster compared to BM25. |
ArgAnalysis35K : A large-scale dataset for Argument Quality Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets in argument quality detection lack quality, quantity and diversity of topics and arguments. |
| Approach: | They propose a dataset that adds a detailed explanation of why the argument made is true, applicable or impactful. |
| Outcome: | The proposed dataset covers 34,890 high-quality argument-analysis pairs and is the largest of its kind to our knowledge. |
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Current evaluations of FEC models that depend on factuality metrics are not reliable and detailed enough. |
| Approach: | They propose a fine-grained evaluation framework that automatically evaluates FEC models on different error categories. |
| Outcome: | The proposed evaluation framework compares models on different error categories and finds the best training modes and significant differences in the performance of existing models. |
Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker (2023.acl-long)
Copied to clipboard
| Challenge: | Empirical results show plug-and-play approach to reason about belief states of multiple characters in reading comprehension tasks is more precise and interpretable than previous approaches. |
| Approach: | They propose a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation. |
| Outcome: | The proposed algorithm improves theory of mind of off-the-shelf neural language models without supervision. |
Don’t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text (2023.acl-long)
Copied to clipboard
| Challenge: | ATINTER model can be used to rewrite adversarial inputs to make them non-adversarial . if undefended, model should maintain good task performance and effectively mitigate adversarials . |
| Approach: | They propose a model that intercepts adversarial inputs and learns to rewrite them . they show that it provides better adversarial robustness than existing defense approaches . |
| Outcome: | The proposed model improves adversarial robustness without compromising task accuracy on a sentiment classification dataset. |
Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring (2023.acl-long)
Copied to clipboard
| Challenge: | Automated Essay Scoring (AES) aims to evaluate the quality score of input essays without human intervention. |
| Approach: | They propose an unsupervised approach to evaluate the quality of input essays . they use multiple heuristic quality signals as pseudo-groundtruths to train a neural AES model . |
| Outcome: | The proposed approach achieves state-of-the-art performance on eight prompts of ASPA dataset compared with previous unsupervised methods . |
Mitigating Label Biases for In-context Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to categorize label biases in in-context learning (ICL) have not addressed all three types of label bias. |
| Approach: | They propose a method that estimates a language model’s label bias using random in-domain words from the task corpus to categorize and detect label biases in ICL. |
| Outcome: | The proposed method significantly improves the performance of GPT-J and GPT-3 on a wide range of tasks. |
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations (2023.acl-long)
Copied to clipboard
| Challenge: | People express information needs with multiple preferences or constraints . modern retrieval systems struggle on such queries, a study finds . |
| Approach: | They construct a dataset of 3357 queries that map to a set of Wikipedia entities . they use crowd-sourced data to match constraints with evidence in documents . |
| Outcome: | The proposed dataset challenges models to match constraints mentioned in queries with evidence in documents and correctly perform various set operations. |
Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for QA use knowledge graphs, but they ignore subgraph optimization and subgraph deepening. |
| Approach: | They propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning that optimizes the structure and knowledge representing of the HKG using a two-stage pruning strategy and knowledge-representation learning. |
| Outcome: | The proposed method improves on existing methods at CommonsenseQA and OpenBookQA. |
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation (2023.acl-long)
Copied to clipboard
| Challenge: | Argument summarisation is a promising but currently under-explored field. |
| Approach: | They propose a framework to generate key points from short texts in a task known as Key Point Analysis. |
| Outcome: | The proposed framework improves state-of-the-art in argument summarisation with performance improvement of 14 percentage points compared to ROUGE and human evaluation scores. |
Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)
Copied to clipboard
Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun
| Challenge: | Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain. |
| Approach: | They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task . |
| Outcome: | The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision. |
Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
| Approach: | They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction. |
| Outcome: | The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation. |
What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric (2023.acl-long)
Copied to clipboard
Enrico Liscio, Oscar Araque, Lorenzo Gatti, Ionut Constantinescu, Catholijn Jonker, Kyriaki Kalimeri, Pradeep Kumar Murukannaiah
| Challenge: | Existing methods to analyze whether a text classifier learns the domain-specific expression of moral language are lacking. |
| Approach: | They propose a method to compare a supervised classifier’s representation of moral rhetoric across domains by exploring similarities and differences between moral concepts and domains. |
| Outcome: | The proposed method compares a supervised classifier’s representation of moral rhetoric across domains and domains. |
Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing word embedding methods fail to model complex word formation well. |
| Approach: | They propose a graph-based relation mining method for OOV word embedding learning that can infer high-quality embeddables for OV words through passing and aggregating semantic attributes and relational information in the WRG. |
| Outcome: | The proposed method outperforms state-of-the-art models on both intrinsic and downstream tasks when faced with OOV words. |
Multimodal Persona Based Generation of Comic Dialogs (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for persona based dialogue generation for comic strips encode two-party dialogues and do not account for visual information. |
| Approach: | They propose a multimodal persona-based architecture to generate dialogues for the next panel in comic strips. |
| Outcome: | The proposed paradigm reduces the perplexity score by 10 points over existing models . the novel dataset, ComSet, contains 54K comic strips . |
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that open-source large language models (LLMs) exhibit diverse strengths and weaknesses due to variations in their architectures and training data. |
| Approach: | They propose a framework that leverages the diverse strengths of open-source large language models. |
| Outcome: | The proposed framework outperforms individual LLMs and baseline methods across various metrics, establishing a substantial performance gap. |
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations. |
| Approach: | They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations. |
| Outcome: | The proposed model can learn from seen attribute values and generalize to unseen combinations. |
Generating Structured Pseudo Labels for Noise-resistant Zero-shot Video Sentence Localization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal. |
| Approach: | They propose a Structure-based Pseudo Label generation (SPL) that generates free-form interpretable pseudo queries before constructing query-dependent event proposals. |
| Outcome: | The proposed method learns with only video data without any annotation . it generates free-form interpretable pseudo queries before constructing query-dependent event proposals . |
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages (2023.acl-long)
Copied to clipboard
Ananya Sai B, Tanay Dixit, Vignesh Nagarajan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre
| Challenge: | Recent studies on machine translation systems focus on high-resource languages, but focus has shifted to low-resourced languages. |
| Approach: | They evaluate 16 metrics from a multidimensional quality metric dataset . they show pre-trained metrics have higher correlations with annotator scores . |
| Outcome: | The proposed evaluations show that pre-trained metrics outperform COMET on Indian languages. |
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases (2023.acl-long)
Copied to clipboard
| Challenge: | Existing debiasing techniques use Counterfactual Data Augmentation (CDA) to balance the training corpus, but this technique slightly modifies the original corpus limiting the representation distance between different demographic groups. |
| Approach: | They propose a two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation to mitigate social biases in PLMs’ encoding. |
| Outcome: | The proposed model outperforms baselines in terms of debiasing performance while maintaining the language modeling capability of PLMs. |
Towards Understanding Omission in Dialogue Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem . |
| Approach: | They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances . |
| Outcome: | The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available . |
Python Code Generation by Asking Clarification Questions (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work addresses text-to-code generation using pretrained language models (PLMs) for large-scale NLD: Logistic Regression. |
| Approach: | They propose a dataset containing pairs of natural language descriptions and code with created synthetic clarification questions and answers to solve the under-specified nature of a natural language description. |
| Outcome: | The proposed model improves on previous models, while introducing new challenges to the community, including when and what clarification questions should be asked. |
A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in natural language processing (NLP) have included attempts to efficiently and effectively comprehend lengthy financial documents. |
| Approach: | They propose a signal-highlighting task that analyzes relationships between financial reports . they also create and publicly release a human-annotated dataset for the task . |
| Outcome: | The proposed pipeline is based on a human-annotated dataset and validates its effectiveness. |
Improving the robustness of NLI models with minimax training (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that our method consistently outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets, while maintaining high in-distance accuracy. |
| Approach: | They propose a minimax objective between a learner model being trained for the task and an auxiliary model aiming to maximize the learner's loss by up-weighting underrepresented "hard" examples with patterns that contradict the shortcuts learned from the prevailing "easy" examples. |
| Outcome: | The proposed method outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets while maintaining high in-distance accuracy. |
USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency parsing . previous studies have cast it as a bottleneck because of overlap and discontinuity issues . |
| Approach: | They propose a bi-lexical dependency parsing graph and a table-filling scheme that addresses overlap and discontinuity issues. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets. |
PAD-Net: An Efficient Framework for Dynamic Networks (2023.acl-long)
Copied to clipboard
| Challenge: | Dynamic networks can significantly improve the model’s representation power with acceptable computational cost. |
| Approach: | They propose a partially dynamic network to transform redundant dynamic parameters into static ones and iterative mode partition to partition dynamic and static parameters efficiently. |
| Outcome: | The proposed network surpasses fully dynamic networks by +0.7% top-1 acc with only 30% dynamic parameters for DY-Conv and +1.9% average score in language understanding with only 50% dynamic parameters. |
Resolving Ambiguities in Text-to-Image Generative Models (2023.acl-long)
Copied to clipboard
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
| Challenge: | ambiguities can lead to misinterpretation and miscommunication in natural language . resolving ambiguity is notoriously hard for machines . |
| Approach: | They propose a framework to disambiguate prompts given to generative models by soliciting clarifications from the end user. |
| Outcome: | The proposed framework generates more faithful images better aligned with user intention in the presence of ambiguities. |
Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that an adversary can extract training data from Pretrained Language Models including Personally Identifiable Information (PII) such as names, phone numbers, and email addresses. |
| Approach: | They propose to use knowledge unlearning to reduce privacy risks for LMs by performing gradient ascent on target token sequences instead of trying to unlearn all the data at once. |
| Outcome: | The proposed method can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being much more efficient and robust. |
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor (2023.acl-long)
Copied to clipboard
| Challenge: | Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions without human supervision. |
| Approach: | They propose to use a dataset of natural language instructions to generate large datasets with no human supervision. |
| Outcome: | The proposed dataset outperforms open-source models on various benchmarks, and is cost-effective. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |
A Survey for Efficient Open Domain Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
| Approach: | They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence. |
| Outcome: | The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence. |
Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities (2023.acl-long)
Copied to clipboard
| Challenge: | linguistically under-represented communities have an extraordinary opportunity to create content in their native languages. |
| Approach: | They propose to solve the problem of script normalization for languages written in a Perso-Arabic script and use a transformer-based model to analyze the noise levels. |
| Outcome: | The proposed model can normalize a language written in a Perso-Arabic script and improve machine translation and language identification tasks. |
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations (2023.acl-long)
Copied to clipboard
| Challenge: | Seq2seq models struggle with compositional generalization in semantic parsing, i.e. generalizing to unseen compositions or deeper recursion of phenomena that the model handles correctly in isolation. |
| Approach: | They propose a new way of parameterizing and predicting permutations by combining input tokens with multisets of output tokens and a method to backpropagate through the solver. |
| Outcome: | The proposed model outperforms pretrained models and prior work on realistic semantic parsing tasks that require generalization to longer examples. |
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning (2023.acl-long)
Copied to clipboard
Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan
| Challenge: | Two-Tower Vision-Language models suffer from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of unil-modal knowledge. |
| Approach: | They propose a model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels to facilitate more comprehensive cross-modal alignment and fusion. |
| Outcome: | The proposed model outperforms baselines with and without Vision-Language Pre-training (VLP) with 4M VLP data. |
Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization. |
| Approach: | They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset. |
| Outcome: | The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters. |
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)
Copied to clipboard
| Challenge: | Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction. |
| Approach: | They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data. |
| Outcome: | The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks. |
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources. |
| Approach: | They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question. |
| Outcome: | The proposed framework outperforms SOTA methods on complex QA datasets. |
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2023.acl-long)
Copied to clipboard
| Challenge: | despite advances in detecting fake news, there is a sizable gap between machine-generated and human-authored fake news . a nave solution is to collect human-written news articles that contain inaccurate information by crawling untrustworthy news media. |
| Approach: | They propose a framework for generating training examples informed by the styles and strategies of human-authored propaganda. |
| Outcome: | The proposed framework improves detection of human-written disinformation by 3.62–7.69% on two public datasets. |
A Length-Extrapolatable Transformer (2023.acl-long)
Copied to clipboard
Yutao Sun, Li Dong, Barun Patra, Shuming Ma, Shaohan Huang, Alon Benhaim, Vishrav Chaudhary, Xia Song, Furu Wei
| Challenge: | Existing Transformers can only deal with the in-distribution size of inputs. |
| Approach: | They propose a relative position embedding to explicitly maximize attention resolution . they also use blockwise causal attention during inference for better resolution a . |
| Outcome: | The proposed model achieves strong performance in interpolation and extrapolation settings. |
A Survey of Deep Learning for Mathematical Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | a survey of deep learning for mathematical reasoning examines the field . a comprehensive reading list is provided to assist readers interested in the field. |
| Approach: | They present a survey of deep learning for mathematical reasoning over the past decade . they outline directions for future research and highlight potential for further exploration . |
| Outcome: | The proposed framework is based on the results of a decade-long survey of deep learning for mathematical reasoning. |
A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training (2023.acl-long)
Copied to clipboard
| Challenge: | Modern Natural Language Generation models come with massive computational and storage requirements. |
| Approach: | They propose a method that applies word-level knowledge distillation to multiple PTs generated by both teacher and student. |
| Outcome: | The proposed techniques can be used to compress natural language models while preserving their performance. |
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation (2023.acl-long)
Copied to clipboard
| Challenge: | Large-scale pre-trained vision-language models have recently achieved tremendous success on a wide range of cross-modal tasks. |
| Approach: | They propose a new framework for a semantically-aware contrastive learning that minimizes the MI between false negative and positive samples . |
| Outcome: | The proposed framework minimizes the MI between false negative samples and positive samples even though they share similar semantics. |
Tell2Design: A Dataset for Language-Guided Floor Plan Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have demonstrated impressive results in generating high-fidelity artistic images. |
| Approach: | They propose a Sequence-to-Sequence model that can serve as a strong baseline for future research. |
| Outcome: | The proposed model can be used as a baseline for future research and human evaluations are conducted on the generated samples and provided an analysis of human performance. |
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations (2023.acl-long)
Copied to clipboard
| Challenge: | Human-annotated labels and explanations are critical for training explainable NLP models. |
| Approach: | They propose a metric that measures the usefulness of an explanation for model performance at both fine-tuning and inference. |
| Outcome: | The proposed metric can evaluate the quality of human-annotated explanations, while Simulatability falls short. |
Rethinking Annotation: Can Language Learners Contribute? (2023.acl-long)
Copied to clipboard
| Challenge: | Researchers have traditionally recruited native speakers to provide annotations for benchmark datasets, but there are languages for which recruiting native speakers is difficult. |
| Approach: | They recruit 36 language learners and provide two types of additional resources and perform mini-tests to measure their language proficiency. |
| Outcome: | The proposed method improves learners' language proficiency in terms of vocabulary and grammar. |
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on multimodal relation extraction (MRE) faces internal-information over-utilization and external-information under-exploitation. |
| Approach: | They propose a framework that implements internal-information screening and external-information exploiting to address these challenges. |
| Outcome: | The proposed framework outperforms the current best model on the benchmark dataset. |
MultiEMO: An Attention-Based Correlation-Aware Multimodal Fusion Framework for Emotion Recognition in Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | Emotion Recognition in Conversations (ERC) is an increasingly popular task in the field of Natural Language Processing. |
| Approach: | They propose a framework that captures cross-modal mapping relationships across modalities . they propose 'multiemotion-aware' framework that integrates multimodal cues into the model . |
| Outcome: | The proposed framework outperforms state-of-the-art models in all emotion categories on two benchmark datasets. |
Learning Language-Specific Layers for Multilingual Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Multilingual Machine Translation (MNMT) is a promising new approach to improve translation quality between non-English languages. |
| Approach: | They propose a language-specific transformer layer to increase model capacity while keeping computation and parameters constant. |
| Outcome: | The proposed approach improves translation quality by 1.3 chrF (1.5 spBLEU) over not using LSLs on a separate decoder architecture. |
Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading. |
| Approach: | They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets. |
| Outcome: | The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans. |
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on text style transfer neglect long style transfer at the discourse level. |
| Approach: | They propose a model that transfers text style into target styles with learnable style embeddings . they use a mask-and-fill framework to explicitly fuse style-specific keywords into generation . |
| Outcome: | The proposed model outperforms baselines in style transfer and content preservation. |
Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent time-dependent question answering datasets tend to be biased in either their coverage of time spans or question types. |
| Approach: | They propose a temporal reasoning framework based on temporal span extraction and time-sensitive reinforcement learning to improve the temporal ability of large language models. |
| Outcome: | The proposed framework improves the temporal reasoning capability of large language models by using temporal span extraction and time-sensitive reinforcement learning. |
Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained Transformer-based language models such as BERT, DeBERTa, and GPT3 have become the go-to tool in NLP. |
| Approach: | They propose an Early-Exit fine-tuning method that assigns each classifier its own set of unique model weights, not updated by other classifiers. |
| Outcome: | The proposed method outperforms Early-Exit and Multi-Model at fast speeds while maintaining comparable scores to Early- Exit at slow speeds. |
Large Language Models Are Reasoning Teachers (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that chain-of-thought (CoT) prompting can elicit language models to solve complex reasoning tasks step-by-step. |
| Approach: | They propose a method that uses large model samples as reasoning teachers to fine-tune smaller models. |
| Outcome: | The proposed method outperforms prompt-based methods and the teacher model in many tasks and extends it by leveraging the teacher's ability to generate multiple rationales for each original sample. |
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches for abductive reasoning in natural language processing rely on manual supervision. |
| Approach: | They propose an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context. |
| Outcome: | The proposed approach outperforms or is comparable to knowledge-augmented zero-shot methods on a diverse set of abductive reasoning datasets. |
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text classification frameworks require large amounts of human-labeled documents to train . |
| Approach: | They propose a contrastive learning framework that improves zero-shot text classification . they add prompts to enhance label retrieval and use retrieved labels to enrich training . |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark text classification datasets. |
Visually-augmented pretrained language models for NLP tasks without images (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to improve pre-trained language models lack visual commonsense and semantics. |
| Approach: | They propose a visual-augmented approach to fine-tune pre-trained language models by using retrieved or generated images instead of relying on explicit images. |
| Outcome: | The proposed approach outperforms baselines on ten tasks and consistently outperformed other approaches. |
Using counterfactual contrast to improve compositional generalization for multi-step quantitative reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | In quantitative question answering, compositional generalization is one of the main challenges of state of the art models. |
| Approach: | They propose a method that uses counterfactual scenarios to generate samples with compositional contrast. |
| Outcome: | The proposed method improves the performance of three state of the art models on four recently released datasets and also improves OOD performance on unseen domains and unsealed compositions. |
A Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization (2023.acl-long)
Copied to clipboard
Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Raghavi Chandu, João Sedoc
| Challenge: | Using crowdsourcing, it is difficult to obtain high-quality annotations for difficult tasks. |
| Approach: | They propose a recruitment pipeline to recruit high-quality Amazon Mechanical Turk workers . they filter out subpar workers before they carry out the evaluations . |
| Outcome: | The proposed method can filter out subpar workers before they carry out evaluations and obtain high-agreement annotations with similar constraints on resources. |
TAVT: Towards Transferable Audio-Visual Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing transfer learning techniques focus on uni-modal analysis and lack consideration of multi-modal content and cross-modal relation. |
| Approach: | They propose a transferable audio-visual text generation framework that incorporates two components: Audio-Visual Meta-Mapper and Dual Counterfactual Contrastive Learning. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods across multiple domains and modal settings. |
MeetingQA: Extractive Question-Answering on Meeting Transcripts (2023.acl-long)
Copied to clipboard
| Challenge: | Meeting transcripts are a promising domain for natural language tasks . lack of annotated data impedes research on other important tasks in this domain . |
| Approach: | They propose an extractive QA dataset comprising questions asked by meeting participants and corresponding responses. |
| Outcome: | The proposed dataset extracts questions asked by meeting participants and corresponding responses from transcripts. |
FERMAT: An Alternative to Accuracy for Numerical Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing numerical reasoning models are too weak for downstream tasks like fact-checking . FERMAT evaluates models on number understanding, mathematical operations, and training dependency . |
| Approach: | They propose a multi-view evaluation set for numerical reasoning in English that evaluates models on key numerical reasoning aspects instead of reporting a single score on a whole dataset. |
| Outcome: | FERMAT evaluates models on number understanding, mathematical operations, and training dependency. |
Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods are biased because of their subjectivity and inconsistent evaluation can misinform the performance of a chat-oriented open-domain dialogue system. |
| Approach: | They propose to use a human evaluation method to estimate the rates of manypasted macro ‘LN’ dialogue system behaviors to compare them with existing evaluation methods. |
| Outcome: | The proposed method is more suitable than alternative Likert-style or comparative approaches for dimensional evaluation of open-domain dialogue systems. |
Decoder Tuning: Efficient Language Understanding as Decoding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to adapt pre-trained models with parameters frozen are based on input-side adaptation, which requires thousands of API queries. |
| Approach: | They propose to train a model-as-a-service (MaaS) setting to provide only the inference APIs for users . they argue that input-side adaptation could be arduous due to the lack of gradient signals . |
| Outcome: | The proposed model outperforms state-of-the-art algorithms with a 200x speed-up. |
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources (2023.acl-long)
Copied to clipboard
Akshatha Arodi, Martin Pömsl, Kaheer Suleman, Adam Trischler, Alexandra Olteanu, Jackie Chi Kit Cheung
| Challenge: | Existing models that make inferences using information from multiple sources are largely understudied . |
| Approach: | They propose a test suite of coreference resolution subtasks that require reasoning over multiple facts and introduce subtask where knowledge is present only at inference time using fictional knowledge. |
| Outcome: | The proposed subtasks differ in terms of which knowledge sources contain the relevant facts and where knowledge is present only at inference time using fictional knowledge. |
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for analyzing and training NLP models have not been integrated to combine their complementary advantages. |
| Approach: | They introduce a framework for selective rationalization and counterfactual text generation that leverages CREST to regularize selective rationales and a loss function that regularizes selective rationals. |
| Outcome: | The proposed framework generates valid counterfactuals that are more natural than those produced by previous methods and can be used for data augmentation at scale. |
Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions. |
| Approach: | They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in the zero-shot directions. |
On Improving Summarization Factual Consistency from Natural Language Feedback (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that language generation models can make errors on fine-grained qualities such as factual consistency. |
| Approach: | They propose to use natural language feedback to improve generation quality and user preference alignment. |
| Outcome: | The proposed model can provide factual consistency in human-edited summaries and further insights into summarization factual consistentness. |
From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | This work sheds light on the theoretical and applied importance of dogwhistles in both NLP and computational social science. |
| Approach: | They propose a typology of dogwhistles, curate a glossary of over 300 dogwhitles and analyze their usage in historical U.S. politicians’ speeches. |
| Outcome: | The proposed model identifies dogwhistles and their meanings and shows that harmful content containing dogwhitles avoids toxicity detection. |
Exploring Large Language Models for Classical Philology (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. |
| Approach: | They propose to use encoder-only and encoder decoder architectures to create four models for Ancient Greek that vary along two dimensions for tasks of interest for Classical languages. |
| Outcome: | The proposed models improve on existing models of Ancient Greek and Latin and provide a large pre-training corpus for Ancient Greek to support the creation of a larger, comparable model zoo for Classical Philology. |
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained models on document images with transformer-based backbones have led to significant performance gains in this field. |
| Approach: | They propose a multi-modal pre-training model that combines text, layout and image . they propose to use local 1D position instead of global 1D positions as layout input . |
| Outcome: | The proposed model can achieve state-of-the-art results on a wide variety of VrDU problems. |
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition (2023.acl-long)
Copied to clipboard
| Challenge: | Existing efforts to improve robustness of audio-visual speech recognition with visual information focus on audio modality . current approaches introduce noise adaptation techniques to improve reliability of AVSR task . |
| Approach: | They propose a visual-invariant modality to strengthen robustness of audio-visual speech recognition (AVSR) it can adapt to any testing noises without dependence on noisy training data, a.k.a., unsupervised noise adaptation. |
| Outcome: | The proposed method outperforms existing state-of-the-arts on visual speech recognition task under various noisy and clean conditions. |
An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Multi-aspect controllable text generation has attracted increasing attention . but the mutual interference of multiple prefixes limits its extensibility to training-time unseen combinations. |
| Approach: | They propose to use trainable gates to normalize the intervention of prefixes to restrain the interference. |
| Outcome: | The proposed approach outperforms baselines on constraint accuracy, text quality, and extensibility. |
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs. |
| Approach: | They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information. |
| Outcome: | The proposed method outperforms a general GNN-based approach for KGC. |
Dual Cache for Long Document Neural Coreference Resolution (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show the effectiveness of cache-based neural coreference resolution models on long documents. |
| Approach: | They propose a hybrid cache that integrates two eviction policies to capture global and local entities separately and improves F1 score of coreference by 0.7 5.7pt. |
| Outcome: | The proposed model outperforms existing models on four benchmarks while saving up to 83% of inference time. |
Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning. |
| Approach: | They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs. |
| Outcome: | The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs. |
DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media (2023.acl-long)
Copied to clipboard
| Challenge: | Mental disorders affect millions of people worldwide and cause interference with their thinking and behavior. |
| Approach: | They propose to adapt a social media-based mental health model to automatically analyze social media content to detect signs of mental disorders. |
| Outcome: | The proposed model improves classification performance and competitiveness against state-of-the-art methods. |
Toward Interactive Dictation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing systems that allow both dictation and editing-by-voice restrict their command language to flat templates invoked by trigger words. |
| Approach: | They propose to allow users to interrupt dictation with spoken editing commands in open-ended natural language. |
| Outcome: | The proposed system can predict edited text with large pre-trained models and predict small programs. |
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
| Approach: | They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks. |
| Outcome: | The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings. |
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning (2023.acl-long)
Copied to clipboard
Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi, Li Dong, Furu Wei, Vishrav Chaudhary, Xia Song
| Challenge: | XY-LENT: X-Y bitext enhanced Language ENcodings achieves state-of-the-art performance over 5 cross-lingual tasks within all model size bands. |
| Approach: | They propose a method for building multilingual representation models that are competitive with existing models and more parameter efficient. |
| Outcome: | The proposed model outperforms XLM-R XXL and is 5x and 6x smaller respectively. |
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023.acl-long)
Copied to clipboard
| Challenge: | Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversation scenes. |
| Approach: | They propose a one-stage end-to-end framework to bridge the information gap between decision-making and question generation in a global understanding manner. |
| Outcome: | The proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. |
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that open-domain dialogue systems are not able to perform well in fast-growing scenarios such as live streaming due to the domain gap between online-post constructed data and those required in downstream conversational tasks. |
| Approach: | They propose to train a conversational agent based on large social media datasets with multiple domains to improve response in live streaming scenarios. |
| Outcome: | The proposed model improves response modeling and addressee recognition in live open-domain scenarios. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
Exploring Lottery Prompts for Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to optimize pre-trained language models are expensive and slow to scale. |
| Approach: | They propose to search for instance-level lottery prompts and generalize them to unseen data . they validate the assumption that for every instance, there is almost always a lottery prompt that induces the correct prediction from the PLM . |
| Outcome: | The proposed method can achieve comparable results with other gradient-free and optimization-free baselines. |
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes. |
| Approach: | They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence. |
| Outcome: | The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset. |
TeAST: Temporal Knowledge Graph Embedding via Archimedean Spiral Timeline (2023.acl-long)
Copied to clipboard
| Challenge: | Existing temporal knowledge graph embedding models fuse temporal information into entities, limiting their effectiveness and potential applications. |
| Approach: | They propose a temporal knowledge graph embedding model which encodes Temporal knowledge graphs via Archimedean Spiral Timeline. |
| Outcome: | The proposed model outperforms existing TKGE methods in terms of relational consistency and interpretability. |
Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that word acquisition is an efficient, supervised, and continual process. |
| Approach: | They develop a computational process for word acquisition through comparative learning . they frame the acquisition of words as representation-symbol mapping . |
| Outcome: | The proposed method can be used to learn the meaning of a word efficiently and efficiently. |
Conjunct Lengths in English, Dependency Length Minimization, and Dependency Structure of Coordination (2023.acl-long)
Copied to clipboard
| Challenge: | a lack of work on the left-to-right order of conjuncts in binary coordinations supports both leftness and closeness to the external head. |
| Approach: | They propose to explain this effect by minimizing the dependence between conjuncts and governors. |
| Outcome: | The proposed explanation provides support for symmetrical dependency structures, as opposed to asymmetrical structures. |
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development (2023.acl-long)
Copied to clipboard
| Challenge: | In this study, we examine the performance of legal-oriented pre-trained language models. |
| Approach: | They conduct a detailed analysis on the performance of legal-oriented pre-trained language models by examining their original objective, acquired knowledge, and legal language understanding capacities. |
| Outcome: | The results show that the models' size and pre-training corpora are important for the development of domain-specific models. |
Revisiting Commonsense Reasoning in Machine Translation: Training, Evaluation and Challenge (2023.acl-long)
Copied to clipboard
| Challenge: | CR is the ability to understand and navigate the world using basic knowledge and understanding shared by most people. |
| Approach: | They propose to incorporate pretrained knowledge into NMT models and use them as robust testbeds for investigating CR in NMT. |
| Outcome: | The proposed method improves the training of NMT models with high CR abilities and provides accurate evaluation metrics. |
NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing backdoor attacks against prompt-based learning involve injecting back doors into embedding layers or word embedders. |
| Approach: | They propose a backdoor attack against prompt-based learning that injects backdoors into embedding layers or word embeddable vectors. |
| Outcome: | The proposed backdoor attack outperforms two state-of-the-art models on six NLP tasks and three prompting strategies. |
Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them. |
| Approach: | They propose to use large language models for RE to evaluate their performance . they use GPT-3 and Flan-T5 large to train RE . |
| Outcome: | The proposed model outperforms existing models on a sequence-to-sequence task under varying levels of supervision. |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
| Outcome: | The proposed method outperforms previous methods on diverse tasks. |
Can Large Language Models Be an Alternative to Human Evaluations? (2023.acl-long)
Copied to clipboard
| Challenge: | Human evaluation is indispensable for assessing the quality of texts generated by machine learning models or written by humans. |
| Approach: | They propose to use large language models to evaluate unseen texts using the same instructions and samples . they also use LLMs to generate responses to questions that are used to conduct human evaluation . |
| Outcome: | The proposed model can be used to evaluate texts in open-ended story generation and adversarial attacks. |
HyperMixer: An MLP-based Low Cost Alternative to Transformers (2023.acl-long)
Copied to clipboard
Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, Francois Marelli, Francois Fleuret, James Henderson
| Challenge: | Existing MLP-based architectures that combine multiple features are expensive and require a lot of training data. |
| Approach: | They propose a simple MLP-based model which allows token mixing by dynamically applying hypernetworks to each feature independently. |
| Outcome: | The proposed model performs better than Transformers and lowers costs in terms of processing time, training data, and hyperparameter tuning. |
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units (2023.acl-long)
Copied to clipboard
Hirofumi Inaguma, Sravya Popuri, Ilia Kulikov, Peng-Jen Chen, Changhan Wang, Yu-An Chung, Yun Tang, Ann Lee, Shinji Watanabe, Juan Pino
| Challenge: | Experimental evaluations show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up. |
| Approach: | They propose a two-pass direct S2ST architecture which generates textual representations and predicts discrete acoustic units . they show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up. |
| Outcome: | The proposed architecture outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up on large datasets. |
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to predict human emotions are inconsistent due to complexity of emotion and subjectivity of perception. |
| Approach: | They propose a Bayesian approach to estimate uncertainty in emotion attributes using a deep neural network model. |
| Outcome: | The proposed approach estimates uncertainty in emotion attributes along with aleatoric and epistemic uncertainties. |
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Discourse connectives are words or phrases that signal the presence of a discourse relation. |
| Approach: | They propose a model that generates discourse connectives between arguments and predicts discourse relations based on the generated connectives. |
| Outcome: | The proposed model outperforms baselines on three datasets and is highly accurate. |
Plug-and-Play Document Modules for Pre-trained Models (2023.acl-long)
Copied to clipboard
Chaojun Xiao, Zhengyan Zhang, Xu Han, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Xiangyang Li, Zhonghua Li, Zhao Cao, Maosong Sun
| Challenge: | Large-scale pre-trained models have been widely adopted for document-oriented NLP tasks, such as question answering. |
| Approach: | They propose to decouple document encoding from downstream tasks by introducing a document plugin into the backbone of a PTM. |
| Outcome: | The proposed model can encode documents once and for all across different scenarios. |
An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models inherit more human-like biases from the training corpora, causing computationally expensive problems. |
| Approach: | They propose parameter-efficient methods in combination with counterfactual data augmentation for bias mitigation. |
| Outcome: | The proposed methods are effective in mitigating gender bias, prompt tuning is more suitable for GPT-2 than BERT, and less effective when it comes to racial and religious bias. |
Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work challenges the bias-variance trade-off . large pretrained models can have large variance and overfit domain-specific data . |
| Approach: | They propose a bias-variance trade-off that implies learning methods need to balance complexity with data size to minimize under-fitting and over-fit. |
| Outcome: | The proposed method achieves strong results on SuperGLUE and clinical information extraction tasks. |
A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing literature demonstrates that compressing deep learning models could affect their fairness. |
| Approach: | They evaluate pruned, distilled, and quantized language models to assess their fairness . they also examine the impact of using multilingual models and evaluation measures . |
| Outcome: | The proposed methods can reduce the fairness of language models by reducing their complexity and reducing the cost of training and deployment. |
Ranking-Enhanced Unsupervised Sentence Representation Learning (2023.acl-long)
Copied to clipboard
Yeon Seonwoo, Guoyin Wang, Changmin Seo, Sajal Choudhary, Jiwei Li, Xiang Li, Puyang Xu, Sunghyun Park, Alice Oh
| Challenge: | Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking. |
| Approach: | They propose a novel unsupervised sentence encoder, RankEncoder, which predicts the semantic vector of an input sentence by leveraging its relationship with other sentences in an external corpus. |
| Outcome: | The proposed unsupervised sentence encoder achieves 80.07% Spearman’s correlation, a 1.1% improvement over the previous state-of-the-art system. |
To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support (2023.acl-long)
Copied to clipboard
| Challenge: | assessing whether and how different claims in a text need to be revised is a hard task, especially for novice writers. |
| Approach: | They propose a sampling strategy based on revision distance to capture differences between versions of the same text. |
| Outcome: | The proposed sampling strategy can be done without additional annotations and judgments. |
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluations of human-in-the-loop systems to combat misinformation are often set up automatically using datasets that were retrospectively constructed. |
| Approach: | They propose a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. |
| Outcome: | The proposed framework is based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. |
Composition-contrastive Learning for Sentence Embeddings (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows potential to learn vector representations from unlabelled data without task-specific fine-tuning. |
| Approach: | They propose to maximize alignment between textual embeddings and a composition of their phrasal constituents. |
| Outcome: | The proposed approach improves on similarity tasks comparable to state-of-the-art approaches. |
Causes and Cures for Interference in Multilingual Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to reduce interference in multilingual machine translation are often computationally intensive and do not always work. |
| Approach: | They propose to reduce interference in multilingual machine translation models by enlarging the model and tuning the sampling temperature to control the proportion of each language pair in the data. |
| Outcome: | The proposed model size, data size, and proportion of each language pair within the dataset determine interference (or synergy) . |
Understanding and Bridging the Modality Gap for Speech Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve end-to-end speech translation (ST) use multitask learning, but there is always a modality gap between ST and MT due to the differences between speech and text. |
| Approach: | They propose a method to bridge the modality gap between ST and MT by leveraging (text) machine translation data. |
| Outcome: | The proposed method bridges the modality gap and achieves significant improvements over baseline in all eight directions. |
Few-shot Reranking for Multi-hop QA via Language Model Prompting (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for multi-hop QA with open-domain questions require a large number of labeled question-document pairs for retrieval. |
| Approach: | They propose a language-based prompt for multi-hop path reranking that relies on language model prompting to generate a relevance score between a question and the path. |
| Outcome: | The proposed method yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples. |
DICE: Data-Efficient Clinical Event Extraction with Generative Models (2023.acl-long)
Copied to clipboard
| Challenge: | EE tasks target specific domains with vague entity boundaries, resulting in a lack of training data. |
| Approach: | They propose a robust and data-efficient generative model for clinical event extraction . they frame event extraction as a conditional generation problem and introduce a contrastive learning objective to decide the boundaries of biomedical mentions. |
| Outcome: | The proposed model is robust and data-efficient for clinical event extraction . it trains an auxiliary mention identification task and event extraction tasks to better identify entity mention boundaries . |
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for cross-lingual semantic parsing are not able to perform tasks on a wide range of datasets. |
| Approach: | They propose a benchmark for cross-lingual semantic parsing that uses 22 natural languages and 8 meaning representations to translate queries into MRs. |
| Outcome: | The proposed benchmarks cover 22 natural languages and 8 meaning representations on 164 domains and 5 tasks covering a wide range of multilingual language models. |
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Neural machine translation models induce a non-smooth representation space, which harms its generalization results. |
| Approach: | They propose a framework to smooth the representation space by adjusting neighbor representations with a small number of new parameters. |
| Outcome: | The proposed framework outperforms the state-of-the-art kNN-MT system with average gains of 1.99 COMET and 1.0 BLEU on four benchmark datasets. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to model multimodal data do not leverage cross-modal information . augmenting input text using cross-module attribute insertions results in poor performance . |
| Approach: | They propose a multimodal deep learning approach that adds visual attributes to inputs to enhance model robustness. |
| Outcome: | The proposed approach is modular, controllable, and task-agnostic. |
Crosslingual Generalization through Multitask Finetuning (2023.acl-long)
Copied to clipboard
Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel
| Challenge: | Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. |
| Approach: | They apply multitask prompted finetuning to pretrained multilingual models and generate variants called BLOOMZ and mT0. |
| Outcome: | The proposed models can generalize to non-English languages that have never been seen before. |
Evaluate AMR Graph Similarity via Self-supervised Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Current AMR metrics do not consider the entire structure of AMR graphs . |
| Approach: | They propose to learn automatic AMR graph similarity evaluation metric by encoding AMR to a pre-trained language model and using GNN adapters to capture structural information of AMR diagrams. |
| Outcome: | The proposed metric significantly improves the correlations with human semantic scores and remains robust under diverse challenges. |
Analyzing Transformers in Embedding Space (2023.acl-long)
Copied to clipboard
| Challenge: | Understanding Transformer-based models has attracted significant attention . a zero-pass approach is feasible for some parameters, and for two-layer attention networks . |
| Approach: | They propose a theoretical framework where parameters of a trained Transformer are interpreted by projecting them into the embedding space. |
| Outcome: | The proposed framework shows that pre-trained and fine-tuned models can be interpreted in embedding space. |
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
Copied to clipboard
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
| Approach: | They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. |
| Outcome: | The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains (2023.acl-long)
Copied to clipboard
Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
| Challenge: | Recent studies have shown that pre-trained language models improve performance on a wide range of NLP tasks. |
| Approach: | They propose to use pre-trained language models to train medical domains on French language to compare performance with specialized ones. |
| Outcome: | The proposed models can take advantage of existing biomedical models in a foreign language by further pre-training them on our targeted data. |
Discriminative Reasoning with Sparse Event Representation for Document-level Event-Event Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level Event Causality Identification (DECI) is a sentence-level task that requires long-text understanding. |
| Approach: | They propose a document-level event causality identification model (SENDIR) that uses sparse attention to capture long-distance dependence. |
| Outcome: | The proposed model can be used to discriminate between event pairs in the same sentence or span multiple sentences. |
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity. |
| Approach: | They propose a benchmark model to detect toxic language by incorporating lexical features into a Chinese dataset to facilitate fine-grained annotations. |
| Outcome: | The proposed model is based on insulting vocabulary containing implicit profanity and is able to detect toxic language with lexical features. |
SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations (2023.acl-long)
Copied to clipboard
Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong, Jingfei Du, Ann Lee, Vedanuj Goswami, Changhan Wang, Juan Pino, Benoît Sagot, Holger Schwenk
| Challenge: | SpeechMatrix is a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. |
| Approach: | They present a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. |
| Outcome: | The proposed model can train bilingual models on 136 language pairs with 418 thousand hours of speech. |
Character-Aware Models Improve Visual Text Rendering (2023.acl-long)
Copied to clipboard
Rosanne Liu, Dan Garrette, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang, Irina Blok, Rj Mical, Mohammad Norouzi, Noah Constant
| Challenge: | Current image generation models struggle to produce well-formed visual text due to lack of character-level input features. |
| Approach: | They conduct a series of experiments to compare character-aware vs. character-blind text encoders to determine their spelling ability. |
| Outcome: | The character-aware models outperform character-blind models on a range of novel text rendering tasks. |
IDRISI-RA: The First Arabic Location Mention Recognition Dataset of Disaster Tweets (2023.acl-long)
Copied to clipboard
| Challenge: | a low resource language such as Arabic is understudied for geolocation extraction . a recent study found that geolocation is underutilized for low resource languages such as arabic . |
| Approach: | They propose a publicly-available Arabic Location Mention Recognition dataset . it provides human- and automatically-labeled versions of tweets in order of thousands and millions of tweet . |
| Outcome: | The proposed dataset provides human- and automatically-labeled versions in order of thousands and millions of tweets. |
FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges. |
| Approach: | They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries . |
| Outcome: | The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs. |
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2023.acl-long)
Copied to clipboard
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman
| Challenge: | Getting sociological beliefs wrong can slow research and lead to wasted effort, missed opportunities, and needless fights. |
| Approach: | They present the results of the NLP Community Metasurvey, run from May to June 2022. |
| Outcome: | The NLP community metasurvey elicited opinions on controversial issues from May to June 2022. |
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |
LENS: A Learnable Evaluation Metric for Text Simplification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing metrics for text simplification are based on unitary or outdated models, making them unsuitable for this approach. |
| Approach: | They present a learnable evaluation metric for text simplification using language models . they also introduce a human evaluation framework that rates simplifications from several models a list-wise manner . |
| Outcome: | The proposed model correlates much better with human judgment than existing metrics. |
MeetingBank: A Benchmark Dataset for Meeting Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | a lack of annotated meeting corpora hinders the development of meeting summarization technology. |
| Approach: | They present a new benchmark dataset of city council meetings over the past decade . they use a divide-and-conquer approach to divide professionally written minutes into shorter passages . |
| Outcome: | The proposed dataset provides a testbed for various meeting summarization systems and allows the public to gain insight into how council decisions are made. |
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
| Approach: | They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks. |
| Outcome: | The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios. |
DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification (2023.acl-long)
Copied to clipboard
| Challenge: | Current text simplification research mostly focuses on English and on sentencelevel simplification. |
| Approach: | They propose to use a dataset of parallel, professionally written and manually aligned simplifications in plain German "plain DE" and "Einfache Sprache" they build a web harvester and experiment with automatic alignment methods to facilitate integration of non-aligned and to be-published parallel documents. |
| Outcome: | The proposed dataset of parallel, professionally written and manually aligned simplifications in plain German is extended to 750 document pairs and 3.5k sentence pairs. |
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text (2023.acl-long)
Copied to clipboard
| Challenge: | Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text, but their performance drops drastically when confronted with linguistically complex texts. |
| Approach: | They propose an end-to-end Neural Divide-and-Conquer Reasoning framework for linguistically complex texts that they struggle to comprehend. |
| Outcome: | The proposed framework significantly improves performance in complex image-text reasoning problem. |
RARR: Researching and Revising What Language Models Say, Using Language Models (2023.acl-long)
Copied to clipboard
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
| Challenge: | Language models (LMs) excel at many tasks but often produce unsupported or misleading content. |
| Approach: | They propose a system that finds attribution for any text generation model and post-edits it to fix unsupported content. |
| Outcome: | The proposed system improves attribution while preserving the original output. |
Program Chairs’ Report on Peer Review at ACL 2023 (2023.acl-long)
Copied to clipboard
| Challenge: | ACL'23 makes its peer review report public and an official part of the conference proceedings. |
| Approach: | They present an analysis of the factors affecting peer review and identify the most problematic issues that the authors complained about. |
| Outcome: | The authors identified the most problematic issues and provided suggestions for the future chairs. |