Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
AdapLeR: Speeding up Inference by Adaptive Length Reduction (2022.acl-long)
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| Challenge: | Pre-trained language models have shown stellar performance in downstream tasks, but their excessive computational costs and high latency hinder their usage in resource-limited settings. |
| Approach: | They propose a method that dynamically eliminates less contributing tokens through layers, resulting in shorter lengths and consequently lower computational cost. |
| Outcome: | The proposed method shows speedups up to 22x during inference time without much sacrifice in performance. |
Quantified Reproducibility Assessment of NLP Results (2022.acl-long)
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| Challenge: | Existing methods for reproducibility assessment are based on concepts and definitions from metrology. |
| Approach: | They propose a method for quantified reproducibility assessment that is based on metrology. |
| Outcome: | The proposed method produces comparable scores across multiple studies . authors find that it facilitates insights into causes of variation between studies - and conclusions can be drawn about improvements. |
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings (2022.acl-long)
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| Challenge: | Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. |
| Approach: | They propose a method to degenerate the learning gradient for rare token embeddings by gating the specific part of the gradient for all tokens during training stage. |
| Outcome: | The proposed method improves the performance of the models but lacks the training dynamics needed to solve the representation degeneration problem. |
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level (2022.acl-long)
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| Challenge: | a recent study shows that large pre-trained language models are not sufficient for Hebrew. |
| Approach: | They propose a large pre-trained language model for Hebrew that recovers morphological segments encoded in contextualized embedding vectors. |
| Outcome: | The proposed model obtains state-of-the-art on all tasks beyond contemporary Hebrew baselines. |
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)
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| Challenge: | Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”. |
| Approach: | They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual. |
| Outcome: | The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions. |
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification (2022.acl-long)
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| Challenge: | Existing datasets for complex word identification (CWI) are limited and the difficulty of the task is augmented by the scarcity of input examples. |
| Approach: | They propose a novel training technique for the complex word identification task based on domain adaptation to improve character and context representations. |
| Outcome: | The proposed training technique improves the target character and context representations and also smooths differences between datasets. |
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)
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| Challenge: | Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets. |
| Approach: | They propose a joint contrastive learning framework to generalize stance features for unseen targets. |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark datasets. |
[CASPI] Causal-aware Safe Policy Improvement for Task-oriented Dialogue (2022.acl-long)
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| Challenge: | Recent advances in off-policy reinforcement learning methods that use offline data as against a simulator have proven to be sample efficient. |
| Approach: | They propose a batch-RL framework for ToD policy learning: Causal-aware Safe Policy Improvement (CASPI) that uses a mechanism to learn fine-grained reward that captures intention behind human response and offers guarantee on dialogue policy’s performance against a baseline. |
| Outcome: | The proposed framework outperforms the current state of the art on an end-to-end dialogue task using a multiwoz2.0 dataset. |
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System (2022.acl-long)
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| Challenge: | Existing studies on multimodal task-oriented dialog systems follow the pipeline to learn intra-modal features separately and then conduct simple feature concatenation or attention-based feature fusion to generate responses. |
| Approach: | They propose a Unified Transformer Semantic Representation framework with feature alignment and intention reasoning for multimodal dialog systems that embed multimodal features into a unified Transformer semantic space to prompt inter-modal interactions. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art approaches on the representative MMD dataset. |
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (2022.acl-long)
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| Challenge: | Existing approaches to model the relations between domains and slots fail to address these issues and can be generalized to unseen domains. |
| Approach: | They propose a Dynamic Schema Graph Fusion Network which generates a dynamic schema graph to explicitly fuse prior slot-domain membership relations and dialogue-aware dynamic slot relations. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets showing that it can extract users' goals or intentions as dialogue states and keep them updated over the whole dialogue. |
Attention Temperature Matters in Abstractive Summarization Distillation (2022.acl-long)
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| Challenge: | Recent progress of abstractive text summarization relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. |
| Approach: | They propose to distill large Transformer summarization models into smaller ones with minimal performance loss by manipulating attention temperatures in Transformers. |
| Outcome: | The proposed method outperforms vanilla pseudo-labeling based methods on three summarization datasets and is shorter and more abstractive. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference (2022.acl-long)
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| Challenge: | No existing methods can achieve effective text segmentation and word discovery in open domain Chinese texts. |
| Approach: | They propose a Bayesian-based method that can achieve effective text segmentation and word discovery in open domain. |
| Outcome: | The proposed method enjoys robust performance and transparent interpretation when no training corpus and domain vocabulary are available. |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature (2022.acl-long)
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| Challenge: | Existing solutions for multi-document summarization ignore potential summary-relevant contents, causing problems in the medical domain. |
| Approach: | They propose a discriminative marginalized probabilistic method to generate a multi-document summary from a cluster of topic-related medical documents using token probability marginalization. |
| Outcome: | The proposed method outperforms the current state-of-the-art on a biomedical dataset for multi-document summarization. |
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm (2022.acl-long)
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Shaoyi Huang, Dongkuan Xu, Ian Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding
| Challenge: | Conventional wisdom in pruning Transformer-based language models is that it reduces model expressiveness, but new research shows pruning increases risk of overfitting when performed at the fine-tuning phase. |
| Approach: | They propose to reduce pruning risk under pretrain-and-finetune paradigm . they propose to use knowledge distillation to improve pruning performance . |
| Outcome: | The proposed method outperforms the leading competitors on the GLUE benchmark. |
CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation (2022.acl-long)
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| Challenge: | a novel data-augmentation technique for neural machine translation is based on a letter substitution cipher . a bijective ciphered text is in effect invisible to modern NLP techniques because of its invariant distributional features . |
| Approach: | They propose a data-augmentation technique for neural machine translation based on ROT-k ciphertexts. |
| Outcome: | The proposed method outperforms existing methods on several datasets by a significant margin. |
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages (2022.acl-long)
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| Challenge: | Pre-trained multilingual models have shown great potential for zero-shot cross-lingual transfer to low web-resource languages (LRLs). |
| Approach: | They propose a vocabulary generation algorithm which enhances lexical overlap across related languages by generating a token that increases the representation of LRLs. |
| Outcome: | The proposed approach improves cross-lingual transfer accuracy without reducing HRL representation and accuracy. |
Long-range Sequence Modeling with Predictable Sparse Attention (2022.acl-long)
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| Challenge: | Existing approaches to capture global context dependencies in sequence modeling suffer from quadratic complexity in time and memory usage. |
| Approach: | They propose an efficient Transformer architecture for fast long-range sequence modeling with a sparse attention matrix and a hidden state cross module. |
| Outcome: | The proposed architecture outperforms the standard multi-head attention and its variants in various long-sequence tasks with low computational costs. |
Improving Personalized Explanation Generation through Visualization (2022.acl-long)
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| Challenge: | Existing explainable recommendation models generate repetitive sentences for different items or empty sentences with insufficient details. |
| Approach: | They propose a visual-enhanced approach to generate rating scores and text explanations using visualization generation and text–image matching discrimination. |
| Outcome: | The proposed approach improves both the text quality and the diversity and explainability of the generated explanations. |
New Intent Discovery with Pre-training and Contrastive Learning (2022.acl-long)
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| Challenge: | Existing methods for identifying intents from unlabeled utterances are label-intensive, inefficient, and inaccurate. |
| Approach: | They propose a multi-task strategy to leverage unlabeled data and external labeled data for representation learning. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three intent recognition benchmarks. |
Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (2022.acl-long)
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| Challenge: | State-level legislation is the cornerstone of national policies and has long-lasting effects on residents of US states. |
| Approach: | They build a dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors. |
| Outcome: | The proposed model predicts winners/losers of bills and then utilizes them to determine the legislative body’s vote breakdown according to demographic/ideological criteria, e.g., gender. |
Structural Characterization for Dialogue Disentanglement (2022.acl-long)
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| Challenge: | tangled multi-party dialogues lead to difficulties in understanding the dialogue history for both human and machine. |
| Approach: | They propose a model for disentangling multi-party dialogues using speaker property and reference dependency. |
| Outcome: | The proposed model achieves state-of-the-art on the Ubuntu IRC benchmark dataset and contributes to dialogue-related comprehension. |
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)
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| Challenge: | Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality. |
| Approach: | They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility. |
| Outcome: | The proposed task is based on a model with static sensibility and dynamic emotion . it achieves state-of-the-art performance in multi-party empathetic dialogue learning . |
MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022.acl-long)
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| Challenge: | Existing methods for emotional support conversation are too coarse-grained to capture user’s instant mental state and focus on expressing empathy in the response rather than gradually reducing user’ s distress. |
| Approach: | They propose a model which firstly infers the user’s fine-grained emotional status and then responds skillfully using a mixture of strategy. |
| Outcome: | The proposed model infers the user’s fine-grained emotional status and responds skillfully using mixed-up strategy modeling. |
GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)
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| Challenge: | Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation. |
| Approach: | They propose a general language model based on autoregressive blank infilling to address this challenge. |
| Outcome: | The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks. |
QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)
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| Challenge: | Existing methods to recommend quotes are evaluated on unpublished datasets . |
| Approach: | They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese. |
| Outcome: | The proposed model outperforms existing methods on all three parts of QuoteR. |
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)
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| Challenge: | a new framework for patent approval prediction is proposed to address this problem . novelty scores are based on comparing an application with millions of prior arts . |
| Approach: | They propose a framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
| Outcome: | The proposed framework unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering (2022.acl-long)
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| Challenge: | Existing knowledge-based visual question answering tasks require weak supervision and no visual knowledge. |
| Approach: | They propose a model which encodes high-level semantics of a question and a knowledge base and learns high order associations between them. |
| Outcome: | The proposed model encodes high-level semantics of a question and a knowledge base, and learns high order associations between them. |
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
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| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
| Approach: | They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences. |
| Outcome: | The proposed model improves naturalness and prosody diversity with clear margins. |
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models (2022.acl-long)
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| Challenge: | Recent work on controlled text generation has required attribute-based fine-tuning of the base language model or restricted the parameterization of the attribute discriminator. |
| Approach: | They propose a global score-based alternative for controllable text generation that combines arbitrary pre-trained black-box models for achieving desired attributes in the generated text. |
| Outcome: | The proposed method outperforms methods that require extra training or fine-tuning . the proposed method is based on a model with energy values of a linear combination of scores from black-box models . |
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer (2022.acl-long)
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| Challenge: | Automated transfer of text between domains does not maintain other attributes between the source and translated text. |
| Approach: | They propose a method for automatic transfer of text between domains that preserves semantic content but changes other attributes. |
| Outcome: | The proposed method retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. |
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning (2022.acl-long)
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| Challenge: | Existing causal reasoning models only learn to induce empirical causal patterns that are predictive to the label, while human beings seek for deep and conceptual understanding of the causality to explain the observed causal facts. |
| Approach: | They present a human-annotated CAusal REasoning dataset with conceptual explanations of the causality. |
| Outcome: | The presented dataset shows that human-annotated explanations can be useful for promoting the accuracy and stability of causal reasoning models. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base (2022.acl-long)
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| Challenge: | Existing semantic parsing frameworks for conversational question answering do not handle uncertain reasoning . qa over large knowledge bases has attracted broad interest due to the popularity of intelligent virtual assistants . |
| Approach: | They propose a fuzzy semantic parsing framework that defines fuzzy comparison operations in grammar for uncertain reasoning based on fuzzy set theory. |
| Outcome: | The proposed framework achieves significant improvements over state-of-the-art models on a large-scale conversational question answering benchmark. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization (2022.acl-long)
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| Challenge: | Existing approaches to describe the syntax structure of code are lacking in retaining the semantic structure of source code. |
| Approach: | They propose to use a triplet position to model hierarchical syntax structure of code by introducing a graph neural network and Transformer to preserve the structural and sequential information of code. |
| Outcome: | The proposed model preserves the structural and sequential information of code and a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation. |
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)
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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang
| Challenge: | Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress. |
| Approach: | They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability. |
| Outcome: | The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability. |
Learn to Adapt for Generalized Zero-Shot Text Classification (2022.acl-long)
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| Challenge: | Existing methods for generalized zero-shot text classification generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes. |
| Approach: | They propose a network that trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning scenario. |
| Outcome: | The proposed model outperforms several previous approaches on five text classification datasets. |
TableFormer: Robust Transformer Modeling for Table-Text Encoding (2022.acl-long)
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| Challenge: | Existing tables models require linearization of the table structure, where row or column order is encoded as an unwanted bias. |
| Approach: | They propose a robust and structurally aware table-text encoding architecture TableFormer where tabular structural biases are incorporated completely through learnable attention biase. |
| Outcome: | The proposed architecture outperforms strong baselines on SQA, WTQ and TabFact table reasoning datasets and achieves state-of-the-art performance on SQ. |
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games (2022.acl-long)
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| Challenge: | Text-based games provide an interactive way to study natural language processing. |
| Approach: | They propose a two-phase training framework to decouple language learning from reinforcement learning and improve the sample efficiency. |
| Outcome: | The proposed method significantly improves performance and sample efficiency against compound error and limited pre-training data. |
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods to translate sentences to other languages using heuristics are challenging. |
| Approach: | They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them. |
| Outcome: | The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics. |
Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
| Approach: | They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones. |
| Outcome: | The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set. |
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022.acl-long)
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Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang
| Challenge: | Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. |
| Approach: | They propose a memory imitation meta-learning method that enhances the model’s reliance on support sets for task adaptation. |
| Outcome: | The proposed method outperforms baselines on both text classification and generation tasks. |
Quality Controlled Paraphrase Generation (2022.acl-long)
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| Challenge: | Recent studies have shown that high quality paraphrases are difficult to generate because of their low flexibility and scalability. |
| Approach: | They propose a quality-guided controlled paraphrase generation model that allows directly controlling the quality dimensions of the generated paraphrase. |
| Outcome: | The proposed method generates paraphrases which maintain original meaning while achieving higher diversity than the uncontrolled baseline. |
Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences (2022.acl-long)
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| Challenge: | Traditionally, example sentences are created by linguistics experts, which are labor-intensive and knowledge-intensive . linguistic experts create dictionary examples for polysemous words, which can be difficult for polygraphs . authors propose a model that generates reasonable examples for targeted words . |
| Approach: | They propose a controllable dictionary example sentence generation model that generates appropriate examples for targeted words . model allows users to provide explicit control over attributes related to readability . |
| Outcome: | The proposed model generates reasonable examples for targeted words even for polysemous words while providing explicit control over readability attributes. |
AraT5: Text-to-Text Transformers for Arabic Language Generation (2022.acl-long)
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| Challenge: | Existing models that convert text-based language problems into text-to-text format are not suitable for multilingual tasks. |
| Approach: | They propose a unified Transformer framework that converts all language problems into a text-to-text format. |
| Outcome: | The proposed model performs better on all ARGEN tasks than existing models with 49 less data. |
Legal Judgment Prediction via Event Extraction with Constraints (2022.acl-long)
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| Challenge: | Existing models fail to locate key event information that determines the judgment results. |
| Approach: | They propose an Event-based Prediction Model with constraints that exploits constraints in LJP. |
| Outcome: | The proposed model surpasses existing models on a standard LJP dataset in English and French. |
Answer-level Calibration for Free-form Multiple Choice Question Answering (2022.acl-long)
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| Challenge: | Pre-trained language models exhibit few-shot and zero-shot learning capability on tasks such as commonsense reasoning. |
| Approach: | They propose to model context-independent biases in terms of the probability of a choice without the context and to remove it using an unsupervised estimate of similarity with the full context. |
| Outcome: | The proposed model improves over baselines on commonsense reasoning tasks. |
Learning When to Translate for Streaming Speech (2022.acl-long)
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| Challenge: | Existing methods waiting-and-translating for a fixed duration break speech acoustic units . Existing models waiting-for a set duration and generating partial sentences are not effective . |
| Approach: | They propose a monotonic segmentation module inside an encoder-decoder model to detect proper speech unit boundaries for a streaming speech input. |
| Outcome: | The proposed method outperforms existing methods on a speech translation dataset and achieves the best trade-off between translation quality and latency. |
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking (2022.acl-long)
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| Challenge: | Recent work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of a large online storage. |
| Approach: | They propose to decouple document-specific and document-independent ranking contributions during codebook-based compression for effective online decompression and embedding composition for better search relevance. |
| Outcome: | The proposed model achieves high relevance and space efficiency with minimal computational cost and low computational complexity. |
Early Stopping Based on Unlabeled Samples in Text Classification (2022.acl-long)
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| Challenge: | Existing methods to stop models from overfitting are based on a separate validation set, but in low resource settings, a small validation set may not be representative enough. |
| Approach: | They propose a method that uses unlabeled samples to estimate the class distribution of the unlabed samples. |
| Outcome: | The proposed method performs better than existing stop-methods in balanced and imbalanced data settings. |
Meta-learning via Language Model In-context Tuning (2022.acl-long)
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| Challenge: | Recent advances in large language models have reduced "task learning and prediction" to a simple sequence prediction problem. |
| Approach: | They propose a meta-learning method that recasts task adaptation and prediction as a sequence prediction problem. |
| Outcome: | The proposed method outperforms MAML on two classification tasks and improves on binaryClfs. |
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books (2022.acl-long)
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| Challenge: | Existing question answering (QA) techniques are created mainly to answer questions asked by humans, but in educational applications, teachers often need to decide what questions to ask . |
| Approach: | They propose to use a fairytale-themed storybook as input to generate QA pairs that can test a student's comprehension skills. |
| Outcome: | The proposed system outperforms state-of-the-art QAG baseline systems and builds an interactive story-telling application for the future real-world deployment. |
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning (2022.acl-long)
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| Challenge: | Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manual designing a comprehensive, high-quality set of labeling rules is tedious and difficult. |
| Approach: | They propose a weakly-supervised learning model that iterates and discovers new labeling rules from data to improve the WSL model. |
| Outcome: | The proposed model outperforms state-of-the-art models on four tasks and bridges the gaps with fully supervised models. |
Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
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| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
| Approach: | They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data. |
| Outcome: | The proposed model achieves state-of-the-art on three standard evaluation corpora. |
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations (2022.acl-long)
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| Challenge: | Existing evaluation metrics for incoherent dialogues are insufficient to accurately reflect incohérence . despite the effectiveness of large pretrained language models, not everyone is into this type of work. |
| Approach: | They propose a Dialogue coherence Evaluation metric that uses Abstract Meaning Representation to apply semantic-level Manipulations for incoherent (negative) data generation. |
| Outcome: | The proposed evaluation metric achieves higher correlations with human judgments compared to baseline methods on dialog datasets by significant margins. |
HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization (2022.acl-long)
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| Challenge: | Document structure is critical for efficient information consumption, but it is difficult to encode it efficiently into the modern Transformer architecture. |
| Approach: | They propose a task which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. |
| Outcome: | The proposed model produces better question-summary hierarchies than comparisons on hierarchy quality and content coverage, the authors show . |
De-Bias for Generative Extraction in Unified NER Task (2022.acl-long)
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| Challenge: | Existing methods for Named entity recognition (NER) are not consistent with the task, which makes the model vulnerable to incorrect biases. |
| Approach: | They propose to use generative model to recognize entities from sentences . they analyze incorrect biases in the generation process from a causal perspective . |
| Outcome: | The proposed method improves the performance of the generative NER model in various datasets. |
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels (2022.acl-long)
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Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda, David Wingate
| Challenge: | Existing prompt engineering methods require labeled data and access to model parameters . a new method for selecting prompt templates without labeles and without direct access to the model is needed. |
| Approach: | They propose a method for selecting prompt templates without labeled examples and without direct access to the model. |
| Outcome: | The proposed method performs at almost oracle levels, without labels, on 7 datasets representing 7 different NLP tasks. |
Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation (2022.acl-long)
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| Challenge: | Recent studies have found that the performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text in a target language. |
| Approach: | They propose to use bilingual lexicons to synthesize textual or labeled data and combine it with monolingual or parallel text when available. |
| Outcome: | The proposed methods improve performance for 19 under-represented languages with and without extra monolingual text. |
Language-agnostic BERT Sentence Embedding (2022.acl-long)
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| Challenge: | Existing methods for learning bilingual sentence embeddings are not well explored. |
| Approach: | They propose to combine best methods for learning multilingual sentence embeddings with pre-trained models to achieve 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba. |
| Outcome: | The proposed model achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, above the 65.5% achieved by LASER. |
Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)
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| Challenge: | Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text. |
| Approach: | They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation. |
| Outcome: | The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities. |
CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLP (2022.acl-long)
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| Challenge: | Existing frameworks for transfer learning across tasks in natural language processing are CRA and CNM . CRA uses a correlation between brain-activity measurement and computational modeling to estimate task similarity with sentence representations. |
| Approach: | They propose a cognitively inspired framework to learn taxonomy for NLP tasks . they use Cognitive Representation Analytics and Cognitive-Neural Mapping . |
| Outcome: | The proposed framework can guide transfer learning across tasks in natural language processing without exhaustive pairwise task transferring. |
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining (2022.acl-long)
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| Challenge: | Large-scale pretrained language models have achieved SOTA results on NLP tasks but are vulnerable to adversarial attacks especially for logographic languages like Chinese. |
| Approach: | They propose a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc. |
| Outcome: | The proposed model outperforms baselines on 5 Chinese NLU tasks without sacrificing performance on clean testsets. |
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)
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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui
| Challenge: | Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query. |
| Approach: | They propose a task where a textual premise is the background presumption on each source image. |
| Outcome: | The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories. |
Parallel Instance Query Network for Named Entity Recognition (2022.acl-long)
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Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang
| Challenge: | Named entity recognition is a fundamental task in natural language processing. |
| Approach: | They propose a method that sets up global and learnable instance queries to extract entities from a sentence in a parallel manner. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on nested and flat datasets. |
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (2022.acl-long)
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| Challenge: | generative dialogue models use dialogue histories to generate the response . however, generating a response based on the historical information is not easy . |
| Approach: | They propose a framework that utilizes simulated dialogue futures to enhance response generation. |
| Outcome: | The proposed framework can generate better responses over strong baselines on two open-domain dialogue datasets. |
Modeling Multi-hop Question Answering as Single Sequence Prediction (2022.acl-long)
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| Challenge: | Existing generative question answering models that leverage passage retrieval with a pre-trained transformer are not effective for multihop QA. |
| Approach: | They propose a generative approach that explicitly models the reasoning process to resolve the answer for multi-hop questions by encoding cross-passage interactions. |
| Outcome: | The proposed model improves on two multi-hop QA datasets and is interpretable. |
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)
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| Challenge: | Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages. |
| Approach: | They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models. |
| Outcome: | The proposed model disassociates semantics from syntax in multilingual models. |
Multi-Granularity Structural Knowledge Distillation for Language Model Compression (2022.acl-long)
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| Challenge: | Existing methods to transfer knowledge to a small model are not enough to represent the rich semantics of a text. |
| Approach: | They propose to distill the knowledge to a student hierarchically across layers using a large teacher-student framework. |
| Outcome: | Experimental results show that the proposed method outperforms distillation methods on GLUE benchmark. |
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022.acl-long)
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| Challenge: | Existing methods to mitigate human-like biases in pretrained language models are based on external corpora and require a distribution alignment loss to mitigate them. |
| Approach: | They propose an automatic method to mitigate biases in pretrained language models by searching for biased prompts such that cloze-style completions are the most different with respect to different demographic groups. |
| Outcome: | The proposed method reduces biases in pretrained language models, including gender and racial bias, and improves fairness of the models. |
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)
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| Challenge: | a dialog system posits that users have figured out clear and specific goals . but in many real-world scenarios, users struggle to figure out specific goals by determining all the necessary slots. |
| Approach: | They propose a mixed-type dialog model with a Prompt-based continual learning mechanism . they collect 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains . |
| Outcome: | The proposed model provides user-goal-related knowledge to help figure out clear and specific goals . it can be extended to any specific type by utilizing existing dialog corpora effectively. |
Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network (2022.acl-long)
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| Challenge: | supervised parsing models have achieved impressive results on in-domain texts, but their performances drop drastically on out-of-domain text due to data distribution shift. |
| Approach: | They propose a dynamic matching network on the shared-private model for semi-supervised cross-domain dependency parsing. |
| Outcome: | The proposed model outperforms baseline models on all domains and achieves state-of-the-art results on all datasets. |
A Closer Look at How Fine-tuning Changes BERT (2022.acl-long)
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| Challenge: | Pre-trained contextualized representations are used to analyze information in NLP . however, how fine-tuning changes the underlying embedding space is less studied . |
| Approach: | They propose to use probing techniques to analyze how fine-tuning changes the embedding space of pre-trained contextualized representations. |
| Outcome: | The proposed model improves classification performance by increasing the distances between examples associated with different labels. |
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval (2022.acl-long)
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| Challenge: | Existing studies focus on improving negative sampling strategy or extra pretraining for dense passage representations, but these studies are not capturing passage with internal representation conflicts. |
| Approach: | They propose a model with a smaller granularity to capture internal representation conflicts . they introduce a negative sampling strategy to encourage a diverse generation of sentence representations within the same passage. |
| Outcome: | The proposed model can be trained on three benchmark datasets to alleviate internal representation conflicts. |
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language (2022.acl-long)
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| Challenge: | Currently, black-box models generate both the proof graph and intermediate inferences within the same model and thus may be unfaithful. |
| Approach: | They propose a transformer-based model that can perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. |
| Outcome: | The proposed model is robust to language perturbations and faster at inference than previous models on existing reasoning datasets. |
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)
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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang
| Challenge: | Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research. |
| Approach: | They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables. |
| Outcome: | The proposed model shows that it is effective in QA and natural language generation over hierarchical tables. |
Doctor Recommendation in Online Health Forums via Expertise Learning (2022.acl-long)
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| Challenge: | Currently, manual doctor allocations are used to handle large volumes of queries, limiting the efficiency to help patients in sheer quantities. |
| Approach: | They propose to use patient queries to model doctor recommendation using their profiles and past dialogues to estimate their capabilities. |
| Outcome: | The proposed model outperforms baseline models on a Chinese online health forum, outperforming baseline models. |
Continual Prompt Tuning for Dialog State Tracking (2022.acl-long)
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| Challenge: | Existing methods to train a model on a sequence of tasks are not efficient enough to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient framework that prevents forgetting and enables knowledge transfer between tasks by learning and freezing a pre-trained model. |
| Outcome: | The proposed framework avoids forgetting and enables knowledge transfer between tasks. |
There’s a Time and Place for Reasoning Beyond the Image (2022.acl-long)
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| Challenge: | Currently, most work in this area is focused on reasoning with local evidence, but there is a gap between a state-of-the-art joint model and human performance. |
| Approach: | They propose a model that can be used to infer, associate, and reason with contextual information from other sources to establish a more complete picture. |
| Outcome: | The proposed model can identify the news articles related to the image through segment-wise understandings of the signs, buildings, crowds, and more. |
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining (2022.acl-long)
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| Challenge: | Tables store rich numerical data, but numerical reasoning over tables is still a challenge. |
| Approach: | They propose a spreadsheet formula is a valuable supervision for numerical reasoning in tables. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative datasets of formula prediction, question answering, and cell type classification. |
Multimodal fusion via cortical network inspired losses (2022.acl-long)
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| Challenge: | Recent work in deep fusion models has led to substantial improvements over unimodal approaches in areas like speech recognition, emotion recognition and analysis. |
| Approach: | They propose to introduce neural dependencies into the loss functions to allow for fusion of different modalities while keeping the model complexity manageable. |
| Outcome: | Experiments on multimodal sentiment analysis tasks show that the proposed approach provides a consistent performance boost. |
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension (2022.acl-long)
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| Challenge: | Procedural Multimodal Documents organize textual instructions and corresponding images step by step. |
| Approach: | They propose a novel temporal-modal entity Graph for comprehending PMDs . they propose encoding and reasoning modules to capture textual and visual entities . |
| Outcome: | The proposed model can capture textual and visual entities and trace their temporal-modal evolution. |
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision. |
| Approach: | They propose to use pre-trained sequence-to-sequence language models to generate graphs . they propose to learn structural constraints and semantics of graphs with limited supervision . |
| Outcome: | The proposed models can learn structural constraints and semantics of graphs with limited supervision. |
Unsupervised Extractive Opinion Summarization Using Sparse Coding (2022.acl-long)
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| Challenge: | Existing methods for opinion summarization rely on human annotations, which may not be feasible. |
| Approach: | They propose to perform opinion summarization in an unsupervised manner by using a dictionary learning algorithm that implicitly captures semantic information from the review text. |
| Outcome: | The proposed algorithm performs well on SPACE and AMAZON datasets and performs controllable summarization to generate aspect-specific summaries using only a few samples. |
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution (2022.acl-long)
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| Challenge: | Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. |
| Approach: | They propose an end-to-end lexical substitution framework based on contextual embedding models that can identify highly-accurate substitute candidates. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models on LS07 and CoInCo benchmark datasets by at least 2% over existing embeddable models. |
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation (2022.acl-long)
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Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Current neural response generation models generate responses directly, omitting unstated implicit knowledge. |
| Approach: | They propose a generative approach to externalize implicit commonsense knowledge and use it to generate responses. |
| Outcome: | Empirical results show that TBS models outperform end-to-end RG models on most automatic metrics and generate more informative, specific, and commonsense-following responses. |
Flow-Adapter Architecture for Unsupervised Machine Translation (2022.acl-long)
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| Challenge: | Recent advances in deep learning have boosted the development of neural machine translation (NMT). |
| Approach: | They propose a flow-adapter architecture for unsupervised neural machine translation that leverages normalizing flows to model distributions of sentence-level latent representations. |
| Outcome: | The proposed model achieves competitive results on several unsupervised MT benchmarks. |
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning (2022.acl-long)
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| Challenge: | Recent unsupervised sentence compression approaches use custom objectives to guide discrete search, but guided search is expensive at inference time. |
| Approach: | They propose to use reinforcement learning to train effective sentence compression models that are also fast when generating predictions. |
| Outcome: | The proposed model outperforms other unsupervised models while being faster at inference time. |
Tracing Origins: Coreference-aware Machine Reading Comprehension (2022.acl-long)
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| Challenge: | a recent study has enriched pre-trained language models with syntactic, semantic and other linguistic information to improve their performance. |
| Approach: | They use a pre-trained language model to leverage coreference information to enhance word embeddings . they use additional encoder layers to focus on coreference mentions or a relational graph convolutional network to model the coreference relations. |
| Outcome: | The proposed model imitates the human reading process and leverages coreference information to enhance word embeddings. |
WatClaimCheck: A new Dataset for Claim Entailment and Inference (2022.acl-long)
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| Challenge: | a dataset for automated fact checking is presented . premise articles are used to verify the veracity of claims . |
| Approach: | They propose a dataset for automated fact checking and an evaluation of state of the art algorithms. |
| Outcome: | The proposed model improves retrieval quality of passages in premise articles . the proposed model predicts claim veracity by inference from premise article . |
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation (2022.acl-long)
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| Challenge: | Existing evaluation metrics are not reliable, but require significant computational resources. |
| Approach: | They propose a method to learn a fixed, low cost version of any expensive NLG metric while retaining most of its original performance. |
| Outcome: | The proposed approach retains most of the original performance while running faster and faster. |
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation (2022.acl-long)
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Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata
| Challenge: | Composition Sampling is a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. |
| Approach: | They propose a method to generate diverse outputs for conditional generation . they use a plan-based neural generation model that is trained to create a composition of the output and then generate by conditioning on it and the input. |
| Outcome: | The proposed method avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain. |
Synthetic Question Value Estimation for Domain Adaptation of Question Answering (2022.acl-long)
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| Challenge: | Existing work adapts QA scores to select high-quality questions, but these scores do not improve QA performance on the target domain. |
| Approach: | They propose to synthesize QA pairs with a question generator on the target domain . they propose to train a Question Value Estimator that estimates usefulness of synthetic questions . |
| Outcome: | The proposed method improves the performance of the target domain QA model by using synthetic questions and only 15% of the human annotations on the targetdomain. |
Better Language Model with Hypernym Class Prediction (2022.acl-long)
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| Challenge: | Class-based language models (LMs) have been devised to address context sparsity in n-gram LMs for decades. |
| Approach: | They propose to use class-based prediction to improve generalization for rare words by annealing from predicting the class to token prediction during training. |
| Outcome: | The proposed model improves perplexity without sacrificing performance on rare words. |
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022.acl-long)
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| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
| Approach: | They propose to use inference operators to analyze social media for fake news spread to uncover unobserved interactions between documents and users' engagement patterns. |
| Outcome: | The proposed algorithms improve the performance of two fake news detection tasks. |
Understanding Gender Bias in Knowledge Base Embeddings (2022.acl-long)
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| Challenge: | Knowledge base (KB) embeddings have been shown to contain gender biases . authors develop two new bias measures to quantify them and trace their origins in KB . |
| Approach: | They propose two ways to quantify gender biases in knowledge base (KB) embeddings . they use the influence function to inspect the contribution of each triple in KB to the overall group bias . |
| Outcome: | The proposed measures are compared with real-world census data to examine gender biases. |
Computational Historical Linguistics and Language Diversity in South Asia (2022.acl-long)
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| Challenge: | South Asian languages are underdocumented, and many with even official administrative status are lowresource (if not data-scarce) linguists must be willing to wrangle data from annotated corpora and grammatical descriptions for endangered languages. |
| Approach: | They argue that data scatteredness is the primary obstacle in the development of South Asian language technology and propose new strategies to break the data barrier. |
| Outcome: | The proposed approach is based on the findings of a recent study on comparative, contact, and historical linguistics in South Asia. |
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization systems still suffer from faithfulness errors, authors say . prior work has proposed models that improve faithfulness, but it is unclear whether this improvement comes from an increased level of extractiveness of the outputs. |
| Approach: | They propose a faithfulness-abstractiveness trade-off curve that serves as a control . they also learn a selector to identify the most faithful and abstractive summary for a given document . |
| Outcome: | The proposed model achieves higher faithfulness scores while being abstractive than the baseline system on two datasets. |
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang (2022.acl-long)
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| Challenge: | a recent study suggests that language evolution is a diachronic process, but no causal analysis is performed to verify these claims. |
| Approach: | They analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang terms. |
| Outcome: | The proposed model shows that slang has smaller semantic change but larger frequency shifts over time. |
Spurious Correlations in Reference-Free Evaluation of Text Generation (2022.acl-long)
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| Challenge: | Recent work suggests that reference-free evaluation metrics may rely on spurious correlations with human judgments. |
| Approach: | They propose to use model-based, reference-free evaluation metrics to evaluate natural language generation systems. |
| Outcome: | The proposed metrics achieve high correlations with human judgments, but they may not be robust enough to evaluate their efficacy and robustness. |
On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
Bias Mitigation in Machine Translation Quality Estimation (2022.acl-long)
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| Challenge: | despite advances in machine translation, the accuracy and fluency of translations cannot be guaranteed without a reference translation. |
| Approach: | They propose to use auxiliary tasks to mitigate partial input bias . they aim to train a multitask architecture with an auxiliary binary classification task . |
| Outcome: | The proposed models reduce partial input bias while maintaining the overall performance. |
Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)
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Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino
| Challenge: | Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations. |
| Approach: | They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. |
| Outcome: | The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task. |
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability (2022.acl-long)
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| Challenge: | Pretrained multilingual models enable zero-shot learning even for unseen languages . current multilingual model covers only a small subset of the world's languages - due to data sparsity, they are not likely to obtain good results for many lowresource languages. |
| Approach: | They ask: how does the number of pretraining languages influence zero-shot learning for unseen languages? do the findings change if the languages used for pretraining are all related? |
| Outcome: | The results show that pretrained models can zero-shot learn for unseen languages even for limited amounts even for low-resource languages. |
Structured Pruning Learns Compact and Accurate Models (2022.acl-long)
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| Challenge: | Pre-trained language models have high costs in terms of storage, memory, and computation time. |
| Approach: | They propose a task-specific structured pruning method CoFi which provides highly parallelizable subnetworks and matches distillation methods in both accuracy and latency. |
| Outcome: | The proposed method matches the distillation methods in accuracy and latency without resorting to unlabeled data. |
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language (2022.acl-long)
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| Challenge: | There are an estimated 6000 to 7000 spoken languages in the world, and at least 43% of them are endangered. |
| Approach: | They propose three principles that may help NLP practitioners foster mutual understanding and collaboration with language communities and three ways in which NLP can potentially assist in language education. |
| Outcome: | The proposed methods can be used to enrich Cherokee language resources with machine-in-the-loop processing and to provide language education. |
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization (2022.acl-long)
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| Challenge: | Prior research on radiology report summarization has focused on single-step end-to-end models which subsume the task of salient content acquisition. |
| Approach: | They propose a two-step extractive summarization followed by abstractive summaries and a new method that breaks down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. |
| Outcome: | The proposed model improves on English radiology reports with an overall improvement in F1 score of 3-4% compared to single-step and two-step-with-single-extractive-process baselines. |
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue (2022.acl-long)
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| Challenge: | Standard conversational semantic parsing maps a user's intent into an executable program, but execution is slow when expensive function calls are included. |
| Approach: | They propose a task of online semantic parsing to predict and execute function calls while the user is still speaking. |
| Outcome: | The proposed approach reduces latency with good parsing quality and execution cost. |
Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures (2022.acl-long)
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| Challenge: | Existing approaches to enrich tabular datasets ignore external unstructured data completely or devise dataset-specific solutions. |
| Approach: | They propose a few-shot transformer-based enrichment framework that reformulates tuples as sentences. |
| Outcome: | The proposed framework outperforms existing fine-tuning solutions on 17 datasets. |
SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2022.acl-long)
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Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang
| Challenge: | Existing methods to handle long text are limited due to time and memory complexity and limited input lengths. |
| Approach: | They propose a multi-stage split-then-summarize framework for long input summarization . their framework can process input text of arbitrary length by adjusting the number of stages . |
| Outcome: | The proposed framework outperforms existing methods on three long meeting summarization datasets and on a long document summarizing dataset. |
Open Domain Question Answering with A Unified Knowledge Interface (2022.acl-long)
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| Challenge: | a retriever-reader framework is popular for open domain question answering . however, accessing heterogeneous knowledge sources through a unified interface remains unknown . |
| Approach: | They propose a retriever-reader framework that uses explicit knowledge to access heterogeneous knowledge sources through a unified interface. |
| Outcome: | The proposed framework can benefit from the expanded knowledge index, the authors show . their approach sets the single-model state-of-the-art on Natural Questions . |
Principled Paraphrase Generation with Parallel Corpora (2022.acl-long)
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| Challenge: | a popular method for paraphrase generation is round-trip machine translation (MT). |
| Approach: | They propose a method that formalizes the implicit similarity function and relaxes it by requiring the entire translation distribution to match . they propose MT can be used to generate paraphrases by decoding back to the source without having to generate pivot translations. |
| Outcome: | The proposed approach is more principled and efficient than round-trip machine translation (MT) and offers an adjustable parameter to control the fidelity-diversity trade-off. |
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems (2022.acl-long)
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| Challenge: | Existing multilingual task-oriented dialogue datasets lack high-quality data curation due to the high expense and challenges of human annotation. |
| Approach: | They propose a method that generates a multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems. |
| Outcome: | The proposed method generates a large-scale multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems. |
Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary Distillation (2022.acl-long)
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| Challenge: | Pretrained language models are used to boost their performance on downstream tasks . pretraining with in-domain texts requires considerable in- domain data and training resources . |
| Approach: | They propose a domain knowledge transferring framework for pre-trained language models without additional in-domain pretraining. |
| Outcome: | The proposed framework extracts domain knowledge from an existing in-domain pretrained language model and transfers it to other PLMs by applying knowledge distillation. |
Retrieval-guided Counterfactual Generation for QA (2022.acl-long)
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| Challenge: | Recent work shows that data augmentation using counterfactuals can help ameliorate this weakness. |
| Approach: | They propose a Retrieve-Generate-Filter technique to generate counterfactuals using QA framework and question generation model trained on original task data. |
| Outcome: | The proposed method improves performance on out-of-domain and challenging evaluation sets over and above existing methods. |
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)
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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev
| Challenge: | Existing models struggle with summarizing long text due to high memory complexity of the full self-attention. |
| Approach: | They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding. |
| Outcome: | The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX. |
Searching for fingerspelled content in American Sign Language (2022.acl-long)
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| Challenge: | Sign language processing is gaining interest in the computer vision and natural language processing communities. |
| Approach: | They propose a model that jointly detects fingerspelling and matches it to a text sequence. |
| Outcome: | The proposed model outperforms baseline methods on a large public dataset of ASL fingerspelling in the wild. |
Skill Induction and Planning with Latent Language (2022.acl-long)
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| Challenge: | a framework for learning hierarchical policies from demonstrations is lacking . we use sparse annotations to guide the discovery of reusable skills . |
| Approach: | They propose a framework for learning hierarchical policies from demonstrations using sparse annotations. |
| Outcome: | The proposed model outperforms other models with access to ground-truth plans in the ALFRED simulation environment. |
Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation (2022.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) is the most popular formalism for Semantic Parsing. |
| Approach: | They propose a language-independent representation of meaning using BabelNet and VerbAtlas. |
| Outcome: | The proposed framework outperforms existing frameworks thanks to fully-semantic framing, the authors show . the proposed dataset is labeled entirely according to the proposed framework, and is available on github. |
Leveraging Similar Users for Personalized Language Modeling with Limited Data (2022.acl-long)
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| Challenge: | Recent work suggests that personalized models are more accurate for individual users than one-size-fits-all solutions. |
| Approach: | They propose a model trained on users that are similar to a new user to find similarity between new and existing users. |
| Outcome: | The proposed model can predict what a user will write when they join a platform and not enough text is available. |
DEEP: DEnoising Entity Pre-training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Earlier named entity translation methods focus on phonetic transliteration, which ignores the sentence context for translation. |
| Approach: | They propose a DEnoising Entity Pre-training method that leverages monolingual data and a knowledge base to improve named entity translation accuracy within sentences. |
| Outcome: | The proposed method improves on three language pairs and denoising auto-encoding baselines. |
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)
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| Challenge: | Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches. |
| Approach: | They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities. |
| Outcome: | The proposed model achieves state-of-the-art in multi-modal sarcasm detection. |
Composable Sparse Fine-Tuning for Cross-Lingual Transfer (2022.acl-long)
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| Challenge: | Adapters and sparse fine-tuning have been developed to improve transfer learning . a number of approaches have been proposed to improve performance of fine-untuners . |
| Approach: | They propose a method that fine-tunes the entire set of parameters of a large pretrained model . they use adapters and sparse fine-uning to improve model efficiency . |
| Outcome: | The proposed method outperforms adapters in cross-lingual transfer benchmarks. |
Toward Annotator Group Bias in Crowdsourcing (2022.acl-long)
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Haochen Liu, Joseph Thekinen, Sinem Mollaoglu, Da Tang, Ji Yang, Youlong Cheng, Hui Liu, Jiliang Tang
| Challenge: | Annotator group bias is a common problem in crowdsourcing, but is often overlooked . |
| Approach: | They propose a probabilistic framework to capture annotator group bias using an extended Expectation Maximization algorithm. |
| Outcome: | The proposed model can model annotator group bias over competitive datasets and demonstrate that it is effective over multiple datasets. |
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
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| Challenge: | grammatical gender languages are characterized by morphosyntactic chains of gender agreement marked on a variety of lexical items and parts-of-speech (POS). |
| Approach: | They propose to enrich the natural, gender-sensitive MuST-SHE corpus with two new linguistic annotation layers to explore gender bias. |
| Outcome: | The proposed models shed light on gender bias and its detection at several levels of granularity. |
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework (2022.acl-long)
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| Challenge: | Existing approaches to open-domain question answering use a rerank-then-read framework . existing approaches use reranked evidence to predict multiple valid answers . |
| Approach: | They propose to use a recall-then-verify framework to solve open-domain questions . the framework separates the reasoning process of each answer to make better use of retrieved evidence . |
| Outcome: | The proposed framework predicts significantly more gold answers on open-domain questions than existing systems that use an oracle reranker. |
Probing as Quantifying Inductive Bias (2022.acl-long)
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| Challenge: | Pre-trained contextual representations have led to performance improvements on downstream tasks. |
| Approach: | They propose a Bayesian framework that quantifies the amount of inductive bias that the representations encode on a specific task. |
| Outcome: | The proposed framework alleviates many problems found in probing and can offer better inductive bias than BERT. |
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency (2022.acl-long)
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| Challenge: | Structured pruning has been extensively studied on monolingual pre-trained models . but little attention has been paid to evaluating the effectiveness of structured pruning on multilingual models. |
| Approach: | They investigate settings, algorithms, and efficiency of structured pruning on multilingual models . authors propose a simple approach that allows training the model once and adapting to different model sizes at inference . |
| Outcome: | The proposed approach allows training the model once and adapting to different model sizes at inference. |
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models (2022.acl-long)
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| Challenge: | Existing methods for fine-tuning large numbers of model parameters have shown impressive performance on the task of discriminating between language produced by cognitively healthy individuals and those with Alzheimer’s disease (AD). |
| Approach: | They propose to use a Transformer DL model pre-trained on general English text to combine an artificially degraded version of itself with a model that generalizes well to spontaneous conversations. |
| Outcome: | The proposed method generalizes well to spontaneous conversations and generates text with characteristics associated with AD, demonstrating the induction of dementia-related linguistic anomalies. |
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on. |
| Approach: | They propose to use Counterfactual Data Augmentation, Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebia as bias mitigation techniques to quantify their effectiveness. |
| Outcome: | The proposed techniques are Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebia. |
Exploring and Adapting Chinese GPT to Pinyin Input Method (2022.acl-long)
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| Challenge: | a frozen GPT can generate state-of-the-art performance on perfect pinyin, but performance drops when input includes abbreviated pinyan, which links to even larger number of Chinese characters. |
| Approach: | They propose to use Chinese GPT to generate fluent sentences using abbreviated pinyin. |
| Outcome: | The proposed approach improves on abbreviated pinyin across all domains. |
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates (2022.acl-long)
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| Challenge: | Existing methods for enhancing pre-trained cross-lingual language models with additional data are rare in practice, especially for low-resource languages. |
| Approach: | They propose a prompt-learning framework for enhancing cross-lingual natural language inference by constructing cloze-style questions through cross-linguistic templates. |
| Outcome: | The proposed framework significantly outperforms existing models under cross-lingual transfer settings. |
Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings (2022.acl-long)
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| Challenge: | Existing studies have evaluated social biases in word embeddings, but they are understudied. |
| Approach: | They propose to evaluate the social biases in sense embeddings using a benchmark dataset for word embedders. |
| Outcome: | The proposed measures show that even when no biases are found at word-level, there are still worrying levels of social biase at sense-level which are often ignored by the word- level bias evaluation measures. |
Hybrid Semantics for Goal-Directed Natural Language Generation (2022.acl-long)
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| Challenge: | Existing goal-directed natural language generation systems use first-order logic to represent semantics, but they are often slow due to the semantics of the partially realized text being checked. |
| Approach: | They propose to use logical semantics and distributional semantics to combine meaning representations to scale a goal-directed natural language generation system without losing expressiveness. |
| Outcome: | The proposed approach scales significantly better than the goal-directed generation system, but it is slower because the representations are not as precise as pure logical semantics. |
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization (2022.acl-long)
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| Challenge: | Clinical trials are a key tool for assessing the effectiveness of health interventions. |
| Approach: | They propose a method to generate informative summaries from PubMed articles . they use the extracted summary to train a BERT-based classifier to predict effectiveness . |
| Outcome: | The proposed method generates informative summaries from multiple documents and trains a classifier based on the summary extracted from the abstracts to predict the effectiveness of the intervention. |
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation (2022.acl-long)
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| Challenge: | Existing IMT systems relying on lexical constrained decoding (LCD) are limited in translation efficiency and quality due to LCD. |
| Approach: | They propose a novel interactive neural machine translation system that uses lexical constraints to decode missing words in a manually revised translation. |
| Outcome: | The proposed system performs significantly better and faster than state-of-the-art IMT on three translation tasks. |
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding (2022.acl-long)
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| Challenge: | Covariate drift can occur when there is a drift between training and testing regarding what users request or how they request it. |
| Approach: | They propose a method that exploits natural variations in data to create a covariate drift in spoken language understanding datasets. |
| Outcome: | The proposed method improves robustness against covariate drift in spoken language understanding (SLU) it shows that a state-of-the-art model suffers performance loss under this drift. |
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph (2022.acl-long)
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| Challenge: | Experimental results show that pre-trained Chinese language models ignore linguistics knowledge to learn representations. |
| Approach: | They propose a task-free enhancement module to integrate linguistics knowledge into Chinese pre-trained language models. |
| Outcome: | The proposed model improves Chinese pre-trained language models on 6 tasks with 10 benchmark datasets. |
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)
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| Challenge: | Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias. |
| Approach: | They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator . |
| Outcome: | The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters. |
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation (2022.acl-long)
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| Challenge: | Existing Text-to-SQL parsers are vulnerable to perturbations in NL questions . we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm . |
| Approach: | They propose to use the Adversarial Table Perturbation to measure robustness of Text-to-SQL parsers against adversarial perturbations. |
| Outcome: | The proposed approach outperforms baseline methods in robustness evaluations on ADVETA and can be used in future projects. |
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions. |
| Approach: | They propose a method that iteratively provides complementary knowledge to student models by dynamically updating teacher models trained on specific data orders. |
| Outcome: | The proposed method improves on multiple machine translation tasks and improves performance over baseline systems. |
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (2022.acl-long)
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| Challenge: | Existing studies on pre-trained language models assume they encode metaphorical knowledge useful for NLP systems. |
| Approach: | They propose to probing metaphoricity information in PLMs and measure their generalization . they find that contextual representations in PMLs encode metaphorical knowledge . |
| Outcome: | The proposed model can encode metaphorical knowledge across languages and datasets . the model can be used to train and test NLP systems . |
Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis (2022.acl-long)
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| Challenge: | Dependency trees are used for aspect-based sentiment classification but are not optimized for aspect classification. |
| Approach: | They propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. |
| Outcome: | The proposed model can achieve competitive performance and interpretability on six English benchmarks and one Chinese dataset. |
Investigating Non-local Features for Neural Constituency Parsing (2022.acl-long)
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| Challenge: | Constituency parsers have been able to achieve competitive performance by using local features. |
| Approach: | They propose to inject non-local features into the training process of a local span-based parser by predicting constituent n-gram non-local patterns and ensuring consistency between constituents and local constituents. |
| Outcome: | The proposed method outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings. |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)
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| Challenge: | Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance. |
| Approach: | They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference. |
| Outcome: | The proposed model outperforms ten strong baseline models and outperformed strong baselines. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
On the Robustness of Question Rewriting Systems to Questions of Varying Hardness (2022.acl-long)
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| Challenge: | entailment : absence of questions classified based on their rewriting hardness or difficulty . enactment of QR system to rewrite context-dependent questions in CQA requires context knowledge . |
| Approach: | They propose a heuristic method to automatically classify questions into subsets of varying hardness . they then conduct a human evaluation to annotate the rewriting hardness of questions . |
| Outcome: | The proposed learning framework improves the overall performance compared to baselines. |
OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages (2022.acl-long)
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| Challenge: | a new study examines the performance of pretraining for sign language recognition in low-resource settings. |
| Approach: | They propose using pose extracted through pretrained models as the standard modality of data to reduce training time and enable efficient inference. |
| Outcome: | The proposed model reduces training time and allows efficient inference in sign languages. |
bert2BERT: Towards Reusable Pretrained Language Models (2022.acl-long)
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Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu
| Challenge: | Pre-training large language models can be expensive and wasteful. |
| Approach: | They propose a method which can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and a two-stage learning method to further accelerate the pre-training. |
| Outcome: | The proposed method can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and significantly improve the pre-training efficiency of the large model. |
Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (2022.acl-long)
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| Challenge: | Existing approaches to multimodal Aspect-Based Sentiment Analysis (MABSA) ignore crossmodalalignment and use pre-trained visual and textual models. |
| Approach: | They propose a multimodal multimodal encoder-decoder framework for MABSA that uses a unified multimodal decoder architecture for all the pretrainingand downstream tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on three MABSA subtasks. |
“You might think about slightly revising the title”: Identifying Hedges in Peer-tutoring Interactions (2022.acl-long)
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| Challenge: | In peer-tutoring, hedges are used to manage rapport and tone down negative feedback . a hybrid approach that outperforms existing baselines is easier to interpret . |
| Approach: | They propose to use a peer-tutoring dataset to identify hedges that manage rapport with teens . they propose to combine pre-trained resources with models that integrate social science insights . |
| Outcome: | The proposed model outperforms existing models while being easier to interpret. |
Efficient Cluster-Based k-Nearest-Neighbor Machine Translation (2022.acl-long)
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| Challenge: | k-Nearest-Neighbor Machine Translation (kNN-MT) is a non-parametric solution for domain adaptation . previous studies have shown that kNN retrieval is at the expense of high latency . |
| Approach: | They propose to use clustering to improve retrieval efficiency by combining a non-parametric MT with an in-domain feature-based retrieval module. |
| Outcome: | The proposed method reduces translation latency by 57% while maintaining the most useful information of the original datastore. |
Headed-Span-Based Projective Dependency Parsing (2022.acl-long)
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| Challenge: | Existing methods for dependency parsing based on headed spans are available. |
| Approach: | They propose a method for projective dependency parsing based on headed spans. |
| Outcome: | The proposed method achieves state-of-the-art or competitive results on PTB, CTB, and UD Dependency parsing is an important task in natural language processing. |
Decoding Part-of-Speech from Human EEG Signals (2022.acl-long)
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| Challenge: | Recent studies have shown that EEG signal magnitude and topography depend on word length, frequency and open vs. closed class. |
| Approach: | They propose to use EEG to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution during text reading. |
| Outcome: | The proposed techniques outperform linear-SVMs on PoS tagging of unigram and bigram data. |
Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
| Approach: | They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search. |
| Outcome: | The proposed method improves on previous work on adversarial robustness evaluation. |
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)
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| Challenge: | Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training. |
| Approach: | They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning. |
| Outcome: | Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing. |
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)
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| Challenge: | Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages. |
| Approach: | They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. |
| Outcome: | The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings (2022.acl-long)
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Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum
| Challenge: | Word2Box provides a set-theoretic training objective for learning word representations . word representation is not natural, all senses and contexts, levels of abstraction, variants and modifications which the word may represent are forced to be captured by mat t is nunc. |
| Approach: | They propose a fuzzy-set interpretation of box embeddings and learn box representations of words using a set-theoretic training objective. |
| Outcome: | The proposed model improves word similarity tasks on less common words. |
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)
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| Challenge: | Argument mining (AM) is a computational process that is used to analyze information in a debating system. |
| Approach: | They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks . |
| Outcome: | The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks . |
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation (2022.acl-long)
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| Challenge: | Existing methods for text generation still suffer from incoherence problems . Neural sequence-to-sequence (seq2sequ) models generate fluent results . |
| Approach: | They propose a novel generation framework that leverages autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. |
| Outcome: | The proposed framework outperforms baseline models and generates more coherent texts with richer contents. |
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)
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| Challenge: | Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. |
| Approach: | They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. |
| Outcome: | The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities. |
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking (2022.acl-long)
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| Challenge: | Experimental results show that task-oriented dialogue systems have attracted growing attention and achieved substantial progress. |
| Approach: | They propose a method that dynamically selects relevant dialogue contents for each slot . they retrieve turn-level utterances and evaluate their relevance to the slot from three perspectives . |
| Outcome: | The proposed method achieves state-of-the-art performance on MultiWOZ 2.1 and MultiWOz 2.2 and superior performance on multiple mainstream benchmark datasets. |
Are Prompt-based Models Clueless? (2022.acl-long)
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| Challenge: | Prompting has reduced the data requirement by reusing the language model head and formatting the task input to match the pre-training objective. |
| Approach: | They propose to examine whether few-shot prompt-based models exploit superficial cues by reusing the model head and formatting the input to match the pre-training objective. |
| Outcome: | The proposed models perform well on instances with superficial cues, but often outperform random accuracy on instances without superficial cuing. |
Learning Confidence for Transformer-based Neural Machine Translation (2022.acl-long)
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| Challenge: | A well-calibrated confidence estimate is not sufficient for neural machine translation (NMT) where probabilities from softmax distribution fail to describe when the model is probably mistaken. |
| Approach: | They propose an unsupervised confidence estimate learning jointly with the training of a neural machine translation model to quantify confidence. |
| Outcome: | The proposed model outperforms standard label smoothing and can predict failures in two real-world scenarios. |
Things not Written in Text: Exploring Spatial Commonsense from Visual Signals (2022.acl-long)
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| Challenge: | Pretrained language models fail in many NLP tasks, but are ineffective in spatial commonsense reasoning. |
| Approach: | They propose a spatial commonsense benchmark that focuses on relative scales of objects and the positional relationship between people and objects under different actions. |
| Outcome: | The proposed framework outperforms pretrained models in answering spatial questions. |
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to improve neural machine translation use token-level adaptive training . however, standard models make predictions on condition of previous contexts . |
| Approach: | They propose a target-context-aware metric which can be supplemented by statistical metrics . they propose an adaptive training approach based on token- and sentence-level CBMI . |
| Outcome: | The proposed model outperforms the Transformer baseline and other similar approaches on English-German and Chinese-English tasks. |
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer (2022.acl-long)
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| Challenge: | Existing sparse attention methods use fixed patterns to select words without considering similarities between words. |
| Approach: | They propose a neural clustering method which integrates into the Self-Attention Mechanism in Transformer and integrates it into the target task. |
| Outcome: | The proposed method outperforms two typical sparse attention methods on translation, text classification, and text matching tasks while having a comparable or even better time and memory efficiency. |
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks (2022.acl-long)
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| Challenge: | Constituency parsing and nested named entity recognition (NER) are similar tasks since they aim to predict a collection of nesting and non-crossing spans. |
| Approach: | They propose a model that uses a pointer network to predict a constituency tree's boundary . constituency parsing is an important task in natural language processing . |
| Outcome: | The proposed model achieves state-of-the-art performance on PTB among all BERT-based models and competitive performance on CTB7 in constituency parsing. |
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)
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| Challenge: | Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words. |
| Approach: | They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data. |
| Outcome: | Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost. |
Dependency Parsing as MRC-based Span-Span Prediction (2022.acl-long)
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| Challenge: | Existing methods for dependency parsing address the issue that edges should be constructed at the text span/subtree level rather than word level. |
| Approach: | They propose a method that constructs dependency trees by directly modeling span-span relations by modeling subtree-subtree relationships. |
| Outcome: | The proposed method constructs dependency trees by modeling span-span relations . it can retrieve missing spans in the span proposal stage, which leads to higher recall . |
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis (2022.acl-long)
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| Challenge: | Existing prompt tuning methods for cross-domain sentiment analysis have been underutilized due to domain discrepancy in the token distributions. |
| Approach: | They propose a new method to model cross-domain sentiment analysis using pre-trained language models by using soft prompts instead of hard templates. |
| Outcome: | The proposed method achieves state-of-the-art results on a publicly available sentiment analysis dataset. |
Generating Scientific Claims for Zero-Shot Scientific Fact Checking (2022.acl-long)
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| Challenge: | Existing methods for scientific fact checking require domain expertise and time consuming. |
| Approach: | They propose a new supervised method for generating claims from scientific sentences and a novel method for negating claims. |
| Outcome: | The proposed method improves on existing methods on biomedical claims and negations. |
Modeling Dual Read/Write Paths for Simultaneous Machine Translation (2022.acl-long)
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| Challenge: | Simultaneous machine translation (SiMT) outputs translation while reading source sentence . existing methods do not direct the read/write path, resulting in poor performance . |
| Approach: | They propose a method which introduces duality constraints to direct the read/write path . they propose to map the read path in two SiMT models to satisfy duality constraint . |
| Outcome: | Experiments on En-Vi and De-En tasks show that the proposed method outperforms baselines under all latency. |
ExtEnD: Extractive Entity Disambiguation (2022.acl-long)
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| Challenge: | Entity disambiguation (ED) is a task in natural language processing that requires a large pre-trained language model to perform. |
| Approach: | They propose a local formulation for Entity Disambiguation (ED) that frames this task as a text extraction problem and propose two Transformer-based architectures that implement it. |
| Outcome: | The proposed model outperforms all its competitors in terms of data efficiency and raw performance on 4 out of 4 benchmarks. |
Hierarchical Sketch Induction for Paraphrase Generation (2022.acl-long)
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| Challenge: | Existing models of paraphrase generation are based on a syntactic sketch, but prior work has included inductive bias. |
| Approach: | They propose a method for learning decompositions of dense encodings as a sequence of discrete latent variables that make iterative refinements of increasing granularity. |
| Outcome: | The proposed model improves on human paraphrase generation by predicting syntactic sketches at test time. |
Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction (2022.acl-long)
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| Challenge: | Existing training data in English is limited to supervised Open Information Extraction (OpenIE) . |
| Approach: | They propose a model that automatically converts English sentences into other languages by using Alignment-Augmented Constrained Translation (AACTrans) they train a generative OpenIE model that outputs for each sentence relations in the first stage and all extractions containing the relation in the second stage. |
| Outcome: | The proposed model outperforms existing models on Spanish, Portuguese, Chinese, Hindi and Telugu on 5 languages. |
Text-to-Table: A New Way of Information Extraction (2022.acl-long)
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| Challenge: | Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data . |
| Approach: | They propose a new problem setting of information extraction, called text-to-table . they formalize text- to-table as a sequence-tosequence problem . |
| Outcome: | The proposed method outperforms existing methods on text-to-table tasks. |
Accelerating Code Search with Deep Hashing and Code Classification (2022.acl-long)
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| Challenge: | Code search is to search reusable code snippets from source code corpus based on natural languages queries. |
| Approach: | They propose a method to accelerate code search with deep hashing and code classification by using deep hashes and code hash. |
| Outcome: | The proposed method can save 90% of retrieval time while preserving at least 99% of retrievals accuracy. |
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022.acl-long)
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| Challenge: | Existing methods for role-oriented dialogue summarization ignore information from other roles, resulting in omitted information. |
| Approach: | They propose a novel method that uses cross attention and decoder self-attention interactions to acquire other roles' critical information. |
| Outcome: | The proposed method significantly outperforms baselines on two public role-oriented dialogue summarization datasets. |
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (2022.acl-long)
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| Challenge: | Existing work on event-centric reasoning fails to model event-level correlations . Existing studies limit their scope to specific scenarios or overlook event- level correlations. |
| Approach: | They propose to pre-train a general Correlation-aware context-to-Event Transformer for event-centric reasoning by highlighting event-level correlations with effective training. |
| Outcome: | The proposed model is applicable to a wide range of event-centric reasoning scenarios, considering its versatility of event correlation types, application formulations, and reasoning types. |
Measuring and Mitigating Name Biases in Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural machine translation systems exhibit problematic biases, such as stereotypical gender bias in occupation terms. |
| Approach: | They propose a method to reduce biases in person name translations by randomly switching entities during translation. |
| Outcome: | The proposed method eliminates the problem without any effect on translation quality. |
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies on self-supervised pretraining for machine translation have focused on the jointly pretrained decoder . |
| Approach: | They propose a method to improve neural machine translation by jointly pretrained decoder . they propose two strategies to remedy the domain and objective discrepancies . |
| Outcome: | The proposed approach improves translation performance and model robustness on three language pairs. |
MSCTD: A Multimodal Sentiment Chat Translation Dataset (2022.acl-long)
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| Challenge: | Multimodal machine translation and textual chat translation have received considerable attention . however, little research has been devoted to multimodal machine translator in conversations . |
| Approach: | They propose a task to generate more accurate translations with the help of dialogue history and visual context. |
| Outcome: | The proposed task can generate more accurate translations with the help of dialogue history and visual context. |
Learning Disentangled Textual Representations via Statistical Measures of Similarity (2022.acl-long)
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| Challenge: | Existing approaches to disentangle a sensitive attribute from textual representations require training and multiple parameter updates. |
| Approach: | They propose a family of regularizers for learning disentangled representations that do not require training. |
| Outcome: | The proposed regularizers are faster and faster and achieve better results when combined with pretrained and randomly initialized text encoders. |
On the Sensitivity and Stability of Model Interpretations in NLP (2022.acl-long)
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| Challenge: | Recent years have witnessed the emergence of post-hoc interpretations that aim to uncover how NLP models make predictions. |
| Approach: | They propose two new criteria that provide complementary notions of faithfulness to removal-based criteria. |
| Outcome: | The proposed methods overcome limitations of gradient-based methods on removal-based criteria and overcome limitations in the proposed methods. |
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark (2022.acl-long)
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| Challenge: | Recent advances in NLP have led to a growing demand for challenging tasks. |
| Approach: | They propose to solve crossword puzzles as a natural language understanding task . they release a corpus of crossword clues from the daily crossword spanning 25 years . |
| Outcome: | The proposed task is based on a corpus of crossword puzzles from the new york times daily crossword spanning 25 years . the dataset contains over half a million unique clue-answer pairs . |
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets (2022.acl-long)
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| Challenge: | Natural language processing models exploit spurious correlations between features and labels in datasets to perform well only within the distributions they are trained on. |
| Approach: | They propose to generate a debiased version of a dataset and replace it with training data to train a model that is generalised to different task distributions. |
| Outcome: | The proposed method outperforms or performs comparable to state-of-the-art debiasing strategies on a large suite of debiased, out-of distribution, and adversarial test sets. |
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)
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| Challenge: | Existing approaches to zero-shot cross-lingual spoken language understanding rely on shared parameters, which can only perform implicit alignment across languages. |
| Approach: | They propose a global-local contrastive learning framework to achieve a fine-grained cross-lingual transfer . they employ bilingual dictionaries to construct multilingual views of the same utterance . |
| Outcome: | Experiments on MultiATIS++ show that GL-CLeF achieves the best performance . GL is based on dictionaries and encourages representations to be more similar than negative example pairs . |
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)
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| Challenge: | masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations. |
| Approach: | They propose a representation learning approach that uses embeddings as anchors to model contextual representations. |
| Outcome: | The proposed model achieves 5x speedup and 1.2 points average improvement over MLM. |
Efficient Hyper-parameter Search for Knowledge Graph Embedding (2022.acl-long)
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| Challenge: | Existing methods for learning knowledge graphs do not search hyper-parameters efficiently. |
| Approach: | They propose an efficient two-stage search algorithm which explores HP configurations on small subgraph and transfers top-performed configurations for fine-tuning on large full graph. |
| Outcome: | The proposed method finds better HPs than baseline algorithms within the same time budget and achieves 9.1% relative improvement on large-scale knowledge graphs. |
A Meta-framework for Spatiotemporal Quantity Extraction from Text (2022.acl-long)
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| Challenge: | a meta-framework for news events that extracts quantities from text is proposed . a previous work on news events focused on extracting event mentions, attributes, and relationships . |
| Approach: | They propose a meta-framework for solving the NLP problem of spatiotemporal quantity extraction . they demonstrate the framework is general and extensible, and shareable crowdsourcing pipeline and baseline models are used . |
| Outcome: | The proposed framework is general and extensible, the authors say . it can extract quantity from news streams, quickly respond to emergencies, investigate incidents . |
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer (2022.acl-long)
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| Challenge: | Pre-trained language models lack visual knowledge of common objects due to reporting bias. |
| Approach: | They investigate whether integrating visual knowledge into a language model can fill the gap . they use captions and images to transfer visual knowledge to 5 downstream tasks . |
| Outcome: | The proposed model can improve performance on 5 tasks that may need visual knowledge to solve the problem. |
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models (2022.acl-long)
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| Challenge: | Recent few-shot learning models such as GPT3 are expensive and slow to deploy for real-world applications. |
| Approach: | They propose a prompt-based low-resource learning method for VL tasks with a few examples . they pre-train a sequence-to-sequence transformer model with prefix and masked language modeling . |
| Outcome: | The proposed method outperforms Frozen on vision-language tasks with prompt-based learning by 18.2% point. |
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022.acl-long)
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| Challenge: | Existing continual relation learning methods rely on labeled training data for learning new tasks, which can be expensive and time-consuming. |
| Approach: | They propose a method that embeds space regularization and data augmentation to learn relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods in CFRL task settings. |
Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution (2022.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) is a way to preserve the semantic meaning of a sentence in a graph. |
| Approach: | They propose a general pretraining method that leverages any general AMR corpus and even automatically parses AMR data to achieve performance gains of up to 6% absolute F1 points. |
| Outcome: | The proposed model significantly improves on the previous state-of-the-art model by up to 11% F1. |
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations (2022.acl-long)
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| Challenge: | Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus. |
| Approach: | They propose to use crowdsourcing annotations to build a large-scale but quality-unguaranteed corpus for opinion expression identification in Chinese. |
| Outcome: | The proposed model can be trained with a synthetic expert and is highly consistent with the training and testing phase. |
Sequence-to-Sequence Knowledge Graph Completion and Question Answering (2022.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embeddable vectors. |
| Approach: | They propose to use an off-the-shelf encoder-decoder Transformer model to generate a knowledge graph embedding model that can be used for KG link prediction and incomplete KG question answering. |
| Outcome: | The proposed model outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning. |
Learning to Mediate Disparities Towards Pragmatic Communication (2022.acl-long)
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| Challenge: | Recent work explores pragmatic reasoning based on Rational Speech Act (RSA) and Theory of Mind in communication (Zhu et al., 2021). |
| Approach: | They propose a framework where the speaker attempts to learn the speaker-listener disparity and adjust the speech accordingly by adding a light-weighted disparity adjustment layer into working memory on top of speaker’s long-term memory system. |
| Outcome: | The proposed framework can learn and adapt to different types of listeners by adding a light-weighted disparity adjustment layer into working memory on top of speaker’s long-term memory system. |
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval (2022.acl-long)
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| Challenge: | Recent research shows that fine-tuning dense retrievers to realize their capacity requires carefully designed fine-cuning techniques. |
| Approach: | They propose a pre-training architecture that learns to condense information into the dense vector through LM pre-training and a coCondenser architecture which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space. |
| Outcome: | The proposed architecture reduces the need for heavy data engineering and large batch training. |
Multimodal Dialogue Response Generation (2022.acl-long)
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Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang
| Challenge: | Existing studies focus on multimodal dialogue models but neglect generation methods. |
| Approach: | They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain. |
| Outcome: | Experiments show that the proposed model can generate informative text and high-resolution image responses. |
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)
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| Challenge: | Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance. |
| Approach: | They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task. |
| Outcome: | The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models. |
| Approach: | They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words . |
| Outcome: | The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores. |
BRIO: Bringing Order to Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization models are often trained with maximum likelihood estimation (MLE) . mLE assumes a deterministic (one-point) target distribution, but can cause performance degradation . |
| Approach: | They propose a new training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality. |
| Outcome: | The proposed model can estimate probabilities of candidate summaries that are more correlated with their level of quality. |
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling (2022.acl-long)
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| Challenge: | Using attention-based models, certain tokens are less ambiguous than others, and they require fewer refinements for disambiguation. |
| Approach: | They propose a lazy transition mechanism to adjust the significance of iterative refinements for each token representation. |
| Outcome: | The proposed model outperforms baseline models on several tasks with the same number of parameters. |
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework (2022.acl-long)
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Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan Stroud, Rada Mihalcea
| Challenge: | Existing video understanding evaluation frameworks that use fill-in-the-blanks do not reflect real-world tasks. |
| Approach: | They propose to use fill-in-the-blanks as a video understanding evaluation framework and introduce a novel dataset that collects multiple perspectives on the same video. |
| Outcome: | The proposed framework does not share the weaknesses of the current state-of-the-art language-informed video understanding tasks, namely: (1) video question answering using multiple-choice questions, where models perform relatively well because they exploit linguistic biases in the task formulation; (2) video captioning, which relies on an open-ended evaluation framework that is often inaccurate because system answers may be perceived as incorrect if they differ in form from the ground truth. |
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling (2022.acl-long)
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| Challenge: | Medical Subject Headings (MeSH) are manually assigned to every biomedical article to facilitate retrieval of relevant information. |
| Approach: | They propose a model that combines new text features with a dynamic knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. |
| Outcome: | The proposed model achieves state-of-the-art on a number of measures. |
A Taxonomy of Empathetic Questions in Social Dialogs (2022.acl-long)
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| Challenge: | Current dialog generation approaches do not model effective question-asking due to the lack of a taxonomy of questions and their purpose in social chitchat. |
| Approach: | They propose to model questions' ability to capture communicative acts and their emotion-regulation intents by annotating a large dataset with established labels. |
| Outcome: | The proposed model can be used to generate labels for the EmpatheticDialogues dataset and to further improve the existing models. |
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)
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| Challenge: | Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations. |
| Approach: | They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task. |
| Outcome: | The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset. |
ProtoTEx: Explaining Model Decisions with Prototype Tensors (2022.acl-long)
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| Challenge: | Neural models for NLP have yielded significant gains in predictive accuracy across tasks. |
| Approach: | They propose a white-box NLP classification architecture based on prototype networks . they propose an interleaved training algorithm that faithfully explains model decisions . |
| Outcome: | The proposed model matches BART-large and exceeds BERTlarge on propaganda detection tasks. |
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)
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| Challenge: | Existing work has treated procedures as shallow structures without modeling the parent-child relation. |
| Approach: | They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB . |
| Outcome: | The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval. |
Cross-Modal Discrete Representation Learning (2022.acl-long)
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| Challenge: | a new framework for learning representations from multimodal data is proposed . the proposed framework uses discretized embedding vectors to capture finer levels of granularity . |
| Approach: | They propose a self-supervised representation learning framework that captures finer levels of granularity across different modalities. |
| Outcome: | The proposed representation can capture finer levels of granularity across different modalities . it can be used on cross-modal retrieval tasks without direct supervision . |
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering (2022.acl-long)
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| Challenge: | Existing work exploits easily accessible co-occurrence information of events to learn event representations. |
| Approach: | They propose a weakly supervised contrastive learning method and a prototype-based clustering method for event representation learning. |
| Outcome: | The proposed framework outperforms baselines on Hard Similarity and Transitive Sentence Similarity tasks. |
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations (2022.acl-long)
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| Challenge: | Large-scale "natural language supervision" using image captions has enabled the first "zero-shot" AI image classifiers, which allow users to create their own image classes using natural language, yet outperform supervised models on common language-and-image tasks. |
| Approach: | They compare the geometry and semantic properties of contextualized English language representations formed by GPT-2 and CLIP, a zero-shot multimodal image classifier which adapts the GPT2 architecture to encode image captions. |
| Outcome: | The proposed classifier outperforms GPT-2 on word-level semantic intrinsic evaluation tasks and achieves a new corpus-based state of the art for the RG65 evaluation. |
ConTinTin: Continual Learning from Task Instructions (2022.acl-long)
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| Challenge: | a new learning paradigm is proposed for NLP, which seeks supervision for solving a target task. |
| Approach: | They propose a new learning paradigm that uses textual instructions to learn new tasks . the main goal of machine learning algorithms lies in seeking supervision for solving a target task. |
| Outcome: | The proposed learning paradigm is based on a stream of more than 60 tasks . it makes full use of task instructions to improve forward-transfer and backward-transference . |
Automated Crossword Solving (2022.acl-long)
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| Challenge: | Using neural question answering models, our system generates answer candidates and then combines loopy belief propagation with local search to find full puzzle solutions. |
| Approach: | They propose a new approach to automatically solving crossword puzzles that uses neural question answering models and loopy belief propagation with local search to find full puzzle solutions. |
| Outcome: | The proposed system outperforms even the best human solvers and can solve crosswords from a wide range of domains with perfect accuracy. |
Learned Incremental Representations for Parsing (2022.acl-long)
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| Challenge: | a new syntactic representation that commits to syntakic choices is proposed for humans . we use a system that uses only incremental processing of a prefix to predict the word in a sentence . |
| Approach: | They propose a syntactic representation that commits to syntakic choices incrementally . they say the system can achieve 93.72 F1 on the Penn Treebank with as few as 5 bits per word . |
| Outcome: | The proposed representation achieves 93.72 F1 on the Penn Treebank with as few as 5 bits per word . the analysis of the representations shows they have interpretable features and deferred resolution of syntactic ambiguities. |
Knowledge Enhanced Reflection Generation for Counseling Dialogues (2022.acl-long)
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| Challenge: | Using retrieval and generative methods, we generate responses using commonsense and domain knowledge. |
| Approach: | They propose a pipeline that collects domain knowledge through web mining and a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
| Outcome: | The proposed pipeline collects domain knowledge through web mining and incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (2022.acl-long)
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Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
| Challenge: | Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers’ reactions to previously unseen news headlines. |
| Approach: | They propose a pragmatic formalism for modeling how readers might react to a news headline . they propose 'misinfo' frames, which can be used to model reader perceptions of news reliability . |
| Outcome: | The proposed model can predict readers' reactions to previously unseen headlines. |
On Continual Model Refinement in Out-of-Distribution Data Streams (2022.acl-long)
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| Challenge: | Existing continual learning (CL) problems cannot cover real-world scenarios such as out-of-distribution errors. |
| Approach: | They propose a continual model refinement problem formulation to solve this problem . they extend several existing continual learning approaches to the CMR problem based on a general sampling algorithm . |
| Outcome: | The proposed model refinement solution improves on existing models and their performance metrics. |
Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection (2022.acl-long)
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| Challenge: | Existing neural dialog models lack specificity and informativeness due to limited knowledge available during training. |
| Approach: | They propose a method to extract relevant knowledge from external sources at decoding time and incorporate it into a dialog response. |
| Outcome: | The proposed method in goal-oriented and knowledge-grounded dialog settings shows that human annotators judge the outputs more engaging and informative compared to responses from prior dialog systems. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
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Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data (2022.acl-long)
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| Challenge: | Experimental results show that REtrieving from the traINing datA only can lead to significant gains on multiple NLG and NLU tasks. |
| Approach: | They propose to retrieve training instances from traINing datA and concatenate them with input to generate output. |
| Outcome: | The proposed method achieves state-of-the-art results on XSum, BigPatent, and CommonsenseQA. |
Life after BERT: What do Other Muppets Understand about Language? (2022.acl-long)
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| Challenge: | Existing pre-trained transformer analysis studies focus on one or two model families at a time, overlooking the variability of the architecture and pre-training objectives. |
| Approach: | They utilize oLMpics bench- mark and psycholinguistic probing datasets for a diverse set of 29 models including T5, BART, and ALBERT. |
| Outcome: | The proposed model fails to resolve compositional questions in a zero-shot fashion, suggesting that pre-training objectives are not predictive of a model’s linguistic capabilities. |
Tailor: Generating and Perturbing Text with Semantic Controls (2022.acl-long)
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| Challenge: | Existing studies train task-specific generators, relying on training a model for every perturbation. |
| Approach: | They propose a semantically-controlled text generation system that modifies sentences to match target attributes. |
| Outcome: | The proposed system produces textual outputs conditioned on control codes derived from semantic representations. |
TruthfulQA: Measuring How Models Mimic Human Falsehoods (2022.acl-long)
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| Challenge: | Existing language models generate false statements that can lead to deception and distrust. |
| Approach: | They propose to measure whether a language model is truthful in generating answers to questions. |
| Outcome: | The proposed model was truthful on 58% of questions, while human performance was 94%. |
Adaptive Testing and Debugging of NLP Models (2022.acl-long)
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| Challenge: | Current approaches to testing and debugging NLP models rely on variable human creativity and extensive labor to instantiate bugs. |
| Approach: | They propose a process which uses large scale language models to automatically write unit tests highlighting bugs in a target model. |
| Outcome: | The proposed process makes users 5-10x more effective at finding bugs than current approaches. |
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning (2022.acl-long)
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| Challenge: | Recent studies show that tabular reasoning models use spurious correlations and focus on false evidence or ignore it altogether. |
| Approach: | They propose a task where models need to extract evidence and then inference labels . they crowdsource evidence row labels and develop unsupervised evidence extraction strategies . |
| Outcome: | The proposed approach outperforms baseline models on the inference task using only the automatically extracted evidence as the premise. |
Interactive Word Completion for Plains Cree (2022.acl-long)
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| Challenge: | a tool that helps users incrementally build complex words is being developed in morphologically complex languages. |
| Approach: | They propose a finite state approach which maps prefixes in a language to completions up to the next morpheme boundary for incremental building of complex words. |
| Outcome: | The proposed approach shows portability to a larger, more complete morphological transducer. |
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing (2022.acl-long)
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| Challenge: | Semantic parsers struggle to generalize to examples with unseen combinations of seen rules from the training set. |
| Approach: | They propose a general framework to produce semantic parses by predicting node labels for a complete multi-layer input-aligned graph. |
| Outcome: | The proposed framework produces better generalizations than the baseline framework . it produces representations directly as a graph and not as sequences . |
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022.acl-long)
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| Challenge: | Toxic language detection systems often falsely flag text that contains minority group mentions as toxic . this over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. |
| Approach: | They develop a machine-generated dataset of toxic and benign statements about 13 minority groups that generates subtly toxic and harmless text with a massive pretrained language model. |
| Outcome: | The proposed method can detect toxic and benign statements on a large scale . it can also detect hate speech on 94.5% of the toxic examples . |
Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)
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Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu
| Challenge: | Existing direct speech-to-speech translation models rely on text generation as an intermediate step. |
| Approach: | They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation. |
| Outcome: | The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision. |
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization (2022.acl-long)
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| Challenge: | State-of-the-art abstractive summarization systems often generate hallucinations, i.e., content that is not directly inferable from the source document. |
| Approach: | They propose a detection approach that separates factual from non-factual hallucinations of entities by masked language models. |
| Outcome: | The proposed method outperforms baselines in accuracy and F1 scores and has a strong correlation with human judgments on factuality classification tasks. |
EntSUM: A Data Set for Entity-Centric Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods for controllable summarization fail to generate entity-centric summaries. |
| Approach: | They propose to use a human-annotated data set EntSUM to generate controllable summarization with a focus on named entities as the aspects to control. |
| Outcome: | The proposed data set shows that existing methods fail to generate entity-centric summaries. |
Sentence-level Privacy for Document Embeddings (2022.acl-long)
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| Challenge: | Existing studies on the definitions of good privacy for natural language use argue that different applications and models require different definitions. |
| Approach: | They propose a technique that uses a combination of statistics and language modeling to produce high (768) dimensional, general -SentDP document embeddings that guarantee a single sentence can be substituted with any other sentence. |
| Outcome: | The proposed method outperforms baseline methods with weaker guarantees like word-level Metric DP and outperformed baseline methods. |
Dataset Geography: Mapping Language Data to Language Users (2022.acl-long)
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| Challenge: | linguistic diversity and coverage of natural language processing systems is a key factor in determining quality of data available in the language field . lack of linguistic, typological, and geographical diversity is acknowledged and documented . but, the advent of massively multilingual models presents opportunity and hope for under-represented languages . |
| Approach: | They analyze the geographical representativeness of NLP datasets to determine their utility . they also explore economic and geographical factors that may explain the observed distributions . |
| Outcome: | The proposed model is representative of the language diversity and coverage of natural language processing systems. |
ILDAE: Instance-Level Difficulty Analysis of Evaluation Data (2022.acl-long)
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| Challenge: | Instance-level difficulty analysis of evaluation data is a new field of research that focuses on leveraging instance difficulty in natural language processing. |
| Approach: | They conduct Instance-Level Difficulty Analysis of Evaluation data in a large-scale setup of 23 datasets and demonstrate its five novel applications. |
| Outcome: | The proposed model improves efficiency and accuracy, improves quality and improves Out-of-Domain performance. |
Image Retrieval from Contextual Descriptions (2022.acl-long)
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| Challenge: | a new multimodal challenge challenges vision-and-language models to integrate context into their representations. |
| Approach: | They propose a multimodal challenge to integrate context into vision-and-language models . they benchmark several state-of-the-art models using cross-encoders and bi-encodings . |
| Outcome: | The proposed model lags behind human models on imageCoDe, compared with human models. |
Multilingual Molecular Representation Learning via Contrastive Pre-training (2022.acl-long)
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| Challenge: | Molecular representation learning is an essential role in cheminformatics, but language model-based approaches focus on local features, hence they may not capture global information. |
| Approach: | They propose a multilingual molecular embedding generation approach that uses two different languages to train a given molecule. |
| Outcome: | The proposed approach is pre-trained using SMILES and IUPAC as two different languages on large-scale molecules. |
Investigating Failures of Automatic Translation
in the Case of Unambiguous Gender (2022.acl-long)
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| Challenge: | Existing models are unable to make basic deductions regarding how to correctly inflect nouns with grammatical gender. |
| Approach: | They propose to evaluate NMT models' ability to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. |
| Outcome: | The proposed model was unable to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. |
Cross-Task Generalization via Natural Language Crowdsourcing Instructions (2022.acl-long)
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| Challenge: | Despite the success of supervised learning, models often struggle with generalization across tasks. |
| Approach: | They propose to use crowdsourcing instructions to build a model that learns a new task by understanding the human-readable instructions that define it. |
| Outcome: | The proposed model can learn from seen tasks and generalize to unseen tasks given its natural crowdsourcing instructions. |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost (2022.acl-long)
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| Challenge: | State-of-the-art NLP systems are brittle when faced with Out-ofVocabulary words . we present a framework that extends word embeddings and makes them robust to OOV . |
| Approach: | They propose a framework that extends existing word embeddings and makes them robust to OOV. |
| Outcome: | The proposed model performs better on original datasets and corrupted variants than previous competitors. |
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks (2022.acl-long)
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Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan
| Challenge: | Existing AI systems fail to perform basic mathematical reasoning when presented in a slightly different manner. |
| Approach: | They propose a multi-task benchmark that evaluates the performance of AI systems on eight different tasks that at their core require simple arithmetic understanding. |
| Outcome: | The proposed benchmark compares the performance of AI systems on eight different tasks. |
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models (2022.acl-long)
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| Challenge: | Large language models and other massively pre-trained "foundation" models can easily adapt to a wide variety of downstream tasks in a process called finetuning. |
| Approach: | They propose to use the bias transfer hypothesis to reduce social biases internalized by large language models during pre-training into harmful task-specific behavior after fine-tuning. |
| Outcome: | The bias transfer hypothesis is the theory that social biases internalized by large language models during pre-training transfer into harmful task-specific behavior after fine-tuning. |
Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement (2022.acl-long)
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| Challenge: | Current methods for detecting dialogue malevolence neglect label correlation. |
| Approach: | They propose to crowdsource a multi-label dataset for detecting malevolent dialogue responses and a model with label correlation enhanced CRF to measure the correlation between malevolence and negative emotions. |
| Outcome: | The proposed model outperforms the best performing baseline method on precision, recall, F1, and Jaccard score by 16.1%, 11.9%, 12.0%, and 6.1% on malevolence. |
How Do We Answer Complex Questions: Discourse Structure of Long-form Answers (2022.acl-long)
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| Challenge: | Recent work explored long-form answers, where answers are free-form texts consisting of multiple sentences. |
| Approach: | They develop an ontology of six sentence-level functional roles for long-form answers . they annotate 3.9k sentences in 640 answer paragraphs and train a strong classifier . |
| Outcome: | The proposed model-generated answers agree less with model-driven answers than human-written answers. |
Understanding Iterative Revision from Human-Written Text (2022.acl-long)
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| Challenge: | This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. |
| Approach: | They propose to annotate iteratively revised text using a multi-domain annotated corpus that generalizes to a variety of domains, edit intentions, revision depths, and granularities. |
| Outcome: | The proposed model improves automatic evaluations by integrating edit intentions with writing quality. |
Making Transformers Solve Compositional Tasks (2022.acl-long)
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| Challenge: | Several studies have reported the inability of Transformer models to generalize compositionally . a key aspect of natural language is the ability to learn basic primitives . |
| Approach: | They propose to use Transformers to generalize compositionally in a large range of tasks . they find that Transformers generalize significantly better than previous models . |
| Outcome: | The proposed models generalize compositionally significantly better than previous models . a set of 12 datasets shows that the proposed models can be improved . |
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation (2022.acl-long)
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| Challenge: | Unlike literal expressions, idioms’ meanings do not follow from their parts, posing a challenge for neural machine translation (NMT). |
| Approach: | They examine the mechanics of the dominant NMT model, Transformer, and their effect on their understanding of idioms. |
| Outcome: | The proposed model over-generates compositional, literal translations and is unable to translate idioms accurately. |
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers (2022.acl-long)
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| Challenge: | Existing datasets for reading comprehension have deterministic answers, but questions in the real world do not always have definite answers. |
| Approach: | They propose a Question Answering (QA) dataset that contains complex questions with conditional answers. |
| Outcome: | The proposed dataset will motivate further research in answering complex questions over long documents. |
Prompt-free and Efficient Few-shot Learning with Language Models (2022.acl-long)
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Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Lambert Mathias, Marzieh Saeidi, Veselin Stoyanov, Majid Yazdani
| Challenge: | Existing methods for few-shot fine-tuning of pretrained language models require carefully engineered prompts and verbalizers to convert inputs into a cloze-format that the PLM can score. |
| Approach: | They propose a method for few-shot fine-tuning of pretrained language models that uses task-specific adapters instead of manually engineered prompts and verbalizers. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on a wide range of few shot NLP tasks. |
Continual Sequence Generation with Adaptive Compositional Modules (2022.acl-long)
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| Challenge: | Existing work on continual sequence generation relies on reuse of existing parameters to learn new tasks or blindly adds new parameters for each new task. |
| Approach: | They propose to use continual sequence generation to reuse existing parameters for new tasks . current continuous learning models often forget knowledge of dissimilar tasks if data distributions shift . |
| Outcome: | The proposed framework outperforms state-of-the-art models on various sequences of generation tasks. |
An Investigation of the (In)effectiveness of Counterfactually Augmented Data (2022.acl-long)
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| Challenge: | Pretrained language models tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. |
| Approach: | They propose to use counterfactually-augmented data (CAD) to identify robust features that are invariant under distribution shift to train models for OOD generalization. |
| Outcome: | The proposed model can learn robust features that are invariant under distribution shifts, but lacks spurious correlations, and may exacerbate existing correlations. |
Inducing Positive Perspectives with Text Reframing (2022.acl-long)
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| Challenge: | Sentiment transfer is a text style transfer task that aims to reverse sentiment polarity and reversal in meaning. |
| Approach: | They propose a task called positive reframing that neutralizes a negative point of view and generates 'positive' perspectives without contradicting original meaning. |
| Outcome: | The proposed model neutralizes a negative point of view and generates 'positive' perspectives without contradicting the original meaning. |
VALUE: Understanding Dialect Disparity in NLU (2022.acl-long)
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| Challenge: | English Natural Language Understanding systems outperform humans on benchmarks like GLUE and SuperGLUE, but they only use textbook Standard American English (SAE) . fewer studies have considered the effects of dialectal differences on performance . |
| Approach: | They propose a benchmark to evaluate the performance of English natural language understanding systems using a set of lexical and morphosyntactic transformation rules. |
| Outcome: | The proposed model outperforms humans on GLUE and SuperGLUE, but only on standard American English . the proposed model recruits fluent speakers of African American vernacular english to validate each feature transformation . |
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer (2022.acl-long)
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| Challenge: | a dataset of English posts with annotations of toxic spans is released . sequence labeling models perform best, but rationale extraction methods are promising . |
| Approach: | They propose a dataset for toxic spans detection that includes an annotation of toxic posts . they propose to add generic rationale extraction mechanisms to the model to obtain toxic span information . |
| Outcome: | The proposed framework is based on a dataset of English posts with toxic span annotations . it shows that sequence labeling models perform best, but that rationale extraction methods are promising . |
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction (2022.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Timothy Dozat, Vincent Perot, Guolong Su, Nan Hua, Joshua Ainslie, Renshen Wang, Yasuhisa Fujii, Tomas Pfister
| Challenge: | Form-like document understanding is a surging research topic due to its practical applications . form documents have unique challenges stemming from their structural characteristics . |
| Approach: | They propose a structure-aware sequence model that leverages spatial relationships between tokens in a form for more precise attention score calculation. |
| Outcome: | The proposed model outperforms existing methods with a more compact model size and less pre-training data. |
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems (2022.acl-long)
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| Challenge: | Moral integrity corpus captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Approach: | They propose a resource that captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Outcome: | The proposed resource captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
Token Dropping for Efficient BERT Pretraining (2022.acl-long)
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| Challenge: | Existing methods to accelerate pretraining of transformer-based models are computationally expensive and degrade performance on downstream tasks. |
| Approach: | They propose a "token dropping" method to accelerate the pretraining of transformer-based models by 25% . they leverage the already built-in masked language modeling loss to identify unimportant tokens with practically no computational overhead. |
| Outcome: | The proposed method reduces the pretraining cost of BERT models by 25% while achieving similar overall performance on downstream tasks. |
DialFact: A Benchmark for Fact-Checking in Dialogue (2022.acl-long)
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| Challenge: | Existing fact-checking models trained on non-dialogue data fail to perform well on this task. |
| Approach: | They propose a task of fact-checking in dialogue to improve fact- checking performance . they propose to use an annotated conversational claim and Wikipedia snippets as evidence . |
| Outcome: | The proposed task improves fact-checking performance in dialogue. |
The Trade-offs of Domain Adaptation for Neural Language Models (2022.acl-long)
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| Challenge: | Neural Language Models (LMs) trained on large generic training sets have been shown to be effective at adapting to smaller, specific target domains for language modeling and other downstream tasks. |
| Approach: | They propose a framework for a Neural Language Models (LM) to be presented in a common framework. |
| Outcome: | The proposed framework highlights similarities and subtle differences between adaptation techniques and the framework. |
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go (2022.acl-long)
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| Challenge: | ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages" focuses on linguistic and sociopolitical challenges facing development of NLP technologies for African languages . |
| Approach: | They propose a typological framework for linguistic and sociopolitical challenges for NLP in African languages. |
| Outcome: | The main objective of this study is to motivate and advocate for an Afrocentric approach to technology development. |
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction (2022.acl-long)
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| Challenge: | Currently, machine translation (MT) is the mainstream approach for GEC. |
| Approach: | They propose to ensemble Transformer-based encoders by majority votes on span-level edits . their best ensemble achieves a new SOTA result even without pre-training on synthetic datasets - "Troy-Blogs" and "Try-1BW". |
| Outcome: | The proposed model achieves a new SOTA result even without pre-training on synthetic datasets. |
Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching (2022.acl-long)
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| Challenge: | Prior approaches for predicting code-switching only consider shallow linguistic context. |
| Approach: | They hypothesize that enriching models with speaker information can guide them to pick up on relevant inductive biases. |
| Outcome: | The proposed model improves on a speaker-driven task in English–Spanish bilingual dialogues by adding sociolinguistically-grounded speaker features as prepended prompts. |
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling (2022.acl-long)
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| Challenge: | a corpus of Spanish newswire rich in unassimilated lexical borrowings is used to identify the language of a word. |
| Approach: | They propose to annotate a corpus of Spanish newswire rich in unassimilated lexical borrowings and evaluate how models perform on this task. |
| Outcome: | The proposed model outperforms models fed with subword embeddings and Transformer-based embeddables on the Spanish newswire corpus. |
Is Attention Explanation? An Introduction to the Debate (2022.acl-long)
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Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin
| Challenge: | Attention has been used in various tasks of NLP and other fields of machine learning to increase performance and provide some explanations. |
| Approach: | They propose to use attention as an explanation for deep learning models to increase performance . they propose to apply attention weights to queries and queries based on scalar scores . |
| Outcome: | The proposed model can be used to increase performance while providing some explanations. |
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)
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| Challenge: | Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge. |
| Approach: | They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop. |
| Outcome: | The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation. |
Neural Pipeline for Zero-Shot Data-to-Text Generation (2022.acl-long)
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| Challenge: | In data-to-text generation, training on in-domain data leads to overfitting and repeating training data noise. |
| Approach: | They propose to train pretrained language models on general-domain text-based operations by transforming single-item descriptions with modules trained on ordering, aggregation, and paragraph compression. |
| Outcome: | The proposed approach enables D2T generation from RDF triples in zero-shot settings. |
Not always about you: Prioritizing community needs when developing endangered language technology (2022.acl-long)
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| Challenge: | low-resource languages lack the quantity of data needed to train statistical and machine learning tools and models. |
| Approach: | They propose to use language technology to support endangered languages' revitalization . they propose to work with indigenous speakers to develop technology for such training . |
| Outcome: | The authors discuss the challenges that researchers and indigenous speech community members face when working together to develop language technology to support endangered languages. |
Automatic Identification and Classification of Bragging in Social Media (2022.acl-long)
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| Challenge: | Bragging is a speech act employed to build a favorable self-image through positive statements about oneself. |
| Approach: | They propose to use tweets annotated for bragging to build a model that can predict bragging with macro F1 up to 72.42 and 35.95 for binary and multi-class bragging classification tasks respectively. |
| Outcome: | The proposed models predict bragging with macro F1 up to 72.42 and 35.95 in binary and multi-class classification tasks respectively. |
Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)
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| Challenge: | Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions . |
| Approach: | They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. |
| Outcome: | The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation. |
Learning Functional Distributional Semantics with Visual Data (2022.acl-long)
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| Challenge: | Functional Distributional Semantics models the meaning of a word as a binary classifier rather than a numerical vector. |
| Approach: | They propose a method to train a Functional Distributional Semantics model with grounded visual data. |
| Outcome: | The proposed model outperforms previous work on learning semantics from Visual Genome on four external evaluation datasets. |
ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding (2022.acl-long)
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| Challenge: | Large language models have shown exciting progress on several NLP benchmarks . however, evaluating their ability for complex analogical reasoning remains under-explored . |
| Approach: | They propose a dataset of narratives for employing proverbs in context as a benchmark for abstract language understanding. |
| Outcome: | The proposed dataset provides fine-grained annotation of aligned spans between proverbs and narratives and contains minimal overlaps between narratives with proverb . the results show that large language models struggle on these tasks compared to humans, and these tasks pose multiple learning challenges. |
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
Characterizing Idioms: Conventionality and Contingency (2022.acl-long)
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| Challenge: | idioms have non-canonical meanings, but non-conventional meanings are contingent on other words . a recent study shows that idiomatic expressions are not homogeneous among idiomas . |
| Approach: | They propose to use a contingency relationship between words in an idiom and non-canonical meanings of words in the idiome. |
| Outcome: | a new study shows that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. |
Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP (2022.acl-long)
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| Challenge: | Existing graph-based text classification methods are based on a Bag-of-Words (BoW) . |
| Approach: | They propose to use a Bag-of-Words-based multi-layer perceptron model to perform inductive text classification using a sequence-based BERT and a lightweight DistilBERT model to fine-tune their results. |
| Outcome: | The proposed model outperforms the current graph-based models and questions the importance of synthetic graphs in modern text classification methods. |
Generative Pretraining for Paraphrase Evaluation (2022.acl-long)
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| Challenge: | ParaBLEU is a paraphrase representation learning model and evaluation metric for text generation. |
| Approach: | They propose a paraphrase representation learning model and evaluation metric for text generation that uses generative conditioning as a pretraining objective. |
| Outcome: | The proposed model outperforms existing models on the 2017 WMT Metrics Shared Task using only 50% of the available training data and surpasses BLEU, ROUGE and METEOR with only 40 examples. |
Incorporating Stock Market Signals for Twitter Stance Detection (2022.acl-long)
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Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
| Challenge: | stance detection is the task of automatically classifying the writer's opinion expressed in a text towards a particular target. |
| Approach: | They propose a robust multi-task neural architecture that combines textual input with high-frequency intra-day time series from stock market prices. |
| Outcome: | The proposed system achieves state-of-the-art on the wt–wt dataset. |
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to train multilingual models to learn the inductive bias of a shared vocabulary and set of parameters across languages. |
| Approach: | They propose to use a multilingual crossover encoder-decoder to fuse language pairs at an instance level to encourage sharing of input and output spaces. |
| Outcome: | The proposed approach improves quality on English-to-Many, Many-to English and zero-shot translation tasks from +0.5 BLEU up to +5.5 BLUE points. |
Word Segmentation as Unsupervised Constituency Parsing (2022.acl-long)
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| Challenge: | Existing theories of word identification from continuous inputs are based on statistical cues, such as Bayesian inference and normative statistics. |
| Approach: | They propose a model which allows for a process isomorphic to unsupervised constituency parsing and which can reproduce human behavior in word identification experiments. |
| Outcome: | The proposed model reproduces human behavior in word identification experiments, suggesting it is viable to study word identification and its relation to syntactic processing. |
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022.acl-long)
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| Challenge: | Several studies discuss the potential harms and benefits of large language models (LLMs) large neural models can replicate and even amplify negative, stereotypical, and derogatory associations in the data. |
| Approach: | They propose to use a first aid kit to assess the safety of conversational AI in various settings . they propose several future directions and discuss ethical considerations . |
| Outcome: | The proposed tools can provide estimates of the relative safety of systems in various settings, but they still have several shortcomings. |
Zero-Shot Cross-lingual Semantic Parsing (2022.acl-long)
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| Challenge: | Recent work in cross-lingual semantic parsing assumes access to high-quality machine translation systems and word alignment tools. |
| Approach: | They propose a multi-task encoder-decoder model to transfer parsing knowledge to additional languages using only English-logical form paired data and in-domain natural language corpora. |
| Outcome: | The proposed model performs significantly above translation-based baselines and competes with the supervised upper-bound. |
The Paradox of the Compositionality of Natural Language: A Neural Machine Translation Case Study (2022.acl-long)
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| Challenge: | Obtaining human-like performance in NLP is often argued to require compositional generalisation. |
| Approach: | They re-instantiate three compositionality tests from the literature and reformulate them for neural machine translation. |
| Outcome: | The proposed models are more compositional than models trained on more data, the authors show . they also show that some non-compositional behaviours are mistakes, whereas others reflect natural variation in data. |
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents (2022.acl-long)
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| Challenge: | Document-level neural machine translation (DocNMT) is a powerful tool for integrating cross-sentence context into translations. |
| Approach: | They explore whether and how contextual modeling in DocNMT is transferable via multilingual modeling. |
| Outcome: | The proposed model can be used to transfer from teacher languages to student languages with no documents but sentence level data. |
Cross-Lingual Phrase Retrieval (2022.acl-long)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences. |
| Approach: | They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |
MMCoQA: Conversational Question Answering over Text, Tables, and Images (2022.acl-long)
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| Challenge: | Existing conversational QA systems only use a single knowledge source, e.g., paragraphs or knowledge graph, and assume it contains enough evidence to extract answers to users' questions. |
| Approach: | They propose a task to answer users' questions with multimodal knowledge sources via multi-turn conversations using a multimodal dataset. |
| Outcome: | The proposed task brings a series of research challenges, including but not limited to priority, consistency, and complementarity of multimodal knowledge. |
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis (2022.acl-long)
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| Challenge: | Structured sentiment analysis is a dependency parsing problem, with imbalanced label distributions and large text spans. |
| Approach: | They propose a novel labeling strategy which contains two sets of token pair labels . they propose tuple extraction problem with a more balanced label distribution . |
| Outcome: | The proposed model outperforms existing models on 5 benchmark datasets in four languages. |
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2022.acl-long)
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| Challenge: | Existing approaches to build labeled training data from domain-specific data are expensive to obtain. |
| Approach: | They propose a Prompt-based Data Augmentation model which only trains small-scale Soft Promptes in frozen Pre-trained Language Models. |
| Outcome: | The proposed model outperforms several baseline models on four benchmarks and is complementary with unlabeled in-domain data. |
Disentangled Sequence to Sequence Learning for Compositional Generalization (2022.acl-long)
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| Challenge: | Existing models struggle to generalize to unseen compositions of seen components . a new approach allows for disentangled representations and better generalization . |
| Approach: | They propose an extension to sequence-to-sequence models which encourage disentanglement by re-encoding source input. |
| Outcome: | The proposed extension delivers better generalization and more disentangled representations . human expressions can be understood by combining known atomic components . |
RST Discourse Parsing with Second-Stage EDU-Level Pre-training (2022.acl-long)
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| Challenge: | Existing pre-trained language models (PLMs) are based on sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU). |
| Approach: | They propose a second-stage EDU-level pre-training approach to learn effective EDU representations continually based on well pre-trained language models. |
| Outcome: | The proposed method improves F1 score by 2.1 points on a benckmark dataset. |
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)
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| Challenge: | Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC) |
| Approach: | They propose three types of negatives to improve contrastive learning to improve learning efficiency. |
| Outcome: | The proposed model outperforms embedding-based methods on several benchmark datasets. |
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze? (2022.acl-long)
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| Challenge: | We compare attention functions in pre-trained language models to human eye fixation patterns during task-specific reading tasks. |
| Approach: | They compare attention functions in large-scale pre-trained language models to classical cognitive models of human attention by using a dataset with eye-tracking recordings of native speakers of English. |
| Outcome: | The proposed model is as predictive of human eye fixation patterns as classical cognitive models of human attention. |
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2022.acl-long)
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Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras
| Challenge: | Laws and their interpretations, legal arguments and agreements are typically expressed in writing. |
| Approach: | They propose a benchmark to evaluate model performance across legal NLU tasks . they also evaluate several generic and legal-oriented models . |
| Outcome: | The proposed model performs better across multiple tasks than previous models. |
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation (2022.acl-long)
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| Challenge: | Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. |
| Approach: | They propose a new benchmark to study semantic biases in Machine Translation of nominal and verbal words in five different languages. |
| Outcome: | The proposed benchmark tests state-of-the-art machine translation systems against the new test bed and provides a statistical and linguistic analysis of the results. |
Improving Word Translation via Two-Stage Contrastive Learning (2022.acl-long)
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| Challenge: | Existing approaches to bilingual lexicon induction (BLI) are limited to two stages, but we propose a robust and effective two-stage contrastive learning framework for the task. |
| Approach: | They propose a two-stage contrastive learning framework for the task . they propose to refine cross-lingual linear maps between static word embeddings via a contrastive objective and integrate it into the self-learning procedure for even more refined cross-linguistic maps. |
| Outcome: | The proposed framework improves cross-lingual maps and word translation capability by integrating it into the self-learning procedure. |
Scheduled Multi-task Learning for Neural Chat Translation (2022.acl-long)
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| Challenge: | Existing methods to improve chat translation focus on modeling bilingual dialogue characteristics . however, there is insufficient data and simple joint training methods . |
| Approach: | They propose a scheduled multi-task learning framework to incorporate large-scale chat translation data into training by adding a second pre-training stage between the original pre- training and fine-tuning stages. |
| Outcome: | The proposed framework improves translation tasks on four language directions. |
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing (2022.acl-long)
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| Challenge: | Using pre-trained language models, we evaluate performance group disparities while none of these techniques guarantee fairness, nor consistently mitigate group disparity. |
| Approach: | They present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks. |
| Outcome: | The proposed methods show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparity. |
Towards Abstractive Grounded Summarization of Podcast Transcripts (2022.acl-long)
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| Challenge: | Podcast summarization is of practical benefit to content providers and consumers . however, podcast summarizing faces significant challenges including factual inconsistencies . speech recognizers induce transcription errors and abstractive summarisation models may hallucinate . |
| Approach: | They propose a method to generate podcast summaries while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
| Outcome: | The proposed method can produce an abstractive summary while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
FiNER: Financial Numeric Entity Recognition for XBRL Tagging (2022.acl-long)
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Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, Georgios Paliouras
| Challenge: | Publicly traded companies are required to submit periodic reports with eXtensive Business Reporting Language (XBRL) word-level tags. |
| Approach: | They propose to use XBRL tagging as a new entity extraction task for the financial domain and release FiNER-139, a dataset of 1.1M sentences with gold X brl tags. |
| Outcome: | The proposed solution replaces numeric expressions with pseudo-tokens reflecting original token shapes and numeric magnitudes. |
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)
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Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan
| Challenge: | Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word. |
| Approach: | They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text. |
| Outcome: | The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks. |
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers (2022.acl-long)
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| Challenge: | Existing neural approaches to solve algebraic word problems have a plausible answer, but this belief has less been verified due to Q. |
| Approach: | They propose a neural model EPT-X which utilizes natural language explanations to solve an algebraic word problem. |
| Outcome: | The proposed model achieves an average performance of 69.59% on a PEN dataset and produces explanations with quality comparable to human output. |
Identifying the Human Values behind Arguments (2022.acl-long)
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| Challenge: | et al., 2003) examines human values in natural language arguments . authors provide a dataset of 5270 arguments from four geographical cultures . |
| Approach: | They propose a multi-level taxonomy of human values with 54 values and a dataset of 5270 arguments from four geographical cultures, manually annotated for human values. |
| Outcome: | The proposed model shows that human values are more diverse than previously thought . it shows that people disagree on the best course forward on controversial issues . |
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation (2022.acl-long)
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| Challenge: | Existing benchmarks for OIE are incomplete and do not include all acceptable variants of the same fact. |
| Approach: | They introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German. |
| Outcome: | The proposed framework is based on fact synsets, clusters, and standardized benchmarks. |
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition (2022.acl-long)
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| Challenge: | Existing methods for audio-visual speech recognition use extra data to increase performance . a recent study shows that the use of unimodal self-supervised learning improves performance on multimodal tasks. |
| Approach: | They propose to use unimodal self-supervised learning to train AVSR models on unlabelled unilateral data. |
| Outcome: | The proposed model improves on lip reading sentences 2 by 30% even without an external language model. |
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization (2022.acl-long)
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| Challenge: | Sequence-to-sequence neural networks have enabled great progress in abstractive summarization. |
| Approach: | They propose to train a second-stage model performing re-ranking on a set of summary candidates by using a mixture of experts. |
| Outcome: | The proposed model outperforms the base model on CNN- DailyMail, XSum and Reddit TIFU with a base PEGASUS. |
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals (2022.acl-long)
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| Challenge: | Current machine learning methods are incapable of efficiently utilizing multimodal information. |
| Approach: | They propose to use text-and-image alignment to improve machine learning's performance on multimodal event sequencing. |
| Outcome: | The proposed models perform significantly worse than humans on multimodal event sequencing than humans. |
Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)
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| Challenge: | Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated. |
| Approach: | They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction. |
| Outcome: | The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results. |
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models (2022.acl-long)
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| Challenge: | Multi-encoder models aim to improve translation quality by encoding document-level contextual information alongside the current sentence. |
| Approach: | They propose to pre-train contextual parameters over split sentence pairs to improve contextual encoding . they propose four different splitting methods to improve learning of contextual parameters . |
| Outcome: | The proposed model improves learning of contextual parameters, both in low and high resource settings. |
Saliency as Evidence: Event Detection with Trigger Saliency Attribution (2022.acl-long)
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| Challenge: | Existing methods to ED see no differences between event types and use a single model to address them all. |
| Approach: | They propose a new concept termed trigger salience attribution which can explicitly quantify the underlying patterns of events. |
| Outcome: | The proposed model can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks. |
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework (2022.acl-long)
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| Challenge: | Existing datasets for emotion detection are heterogeneous in size, domain, format, splits, emotion categories and role labels, hampering progress in this area. |
| Approach: | They propose a framework for annotating emotions manually using a common labeling scheme to unify several datasets tagged with emotions and semantic roles. |
| Outcome: | The proposed framework unifies datasets tagged with emotions and semantic roles by using a common labeling scheme. |
Context Matters: A Pragmatic Study of PLMs’ Negation Understanding (2022.acl-long)
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| Challenge: | In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. |
| Approach: | They propose to use transformer-based pre-trained language models to study negation understanding using a pragmatic paradigm. |
| Outcome: | The proposed transformer-based model outperforms the human benchmark at NLU and GLUE, and the results are much more optimistic than previous studies. |
Probing for Predicate Argument Structures in Pretrained Language Models (2022.acl-long)
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| Challenge: | Recent proposed approaches have achieved impressive results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL) |
| Approach: | They propose to probe for predicate argument structures in pretrained language models . they show that PLMs encode semantic structures directly into contextualized representations . |
| Outcome: | The proposed models have achieved impressive results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL) |
Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction (2022.acl-long)
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| Challenge: | Existing models for zero-shot cross-lingual event argument extraction are based on pre-trained generative language models. |
| Approach: | They propose to use pre-trained generative language models to generate sentences that fill in a template with arguments extracted from the input passage. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on zero-shot cross-lingual EAE. |
Identifying Moments of Change from Longitudinal User Text (2022.acl-long)
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| Challenge: | Identifying changes in individuals’ behaviour and mood via shared content is gaining importance given the global increase in mental health disorders and the limited access to support services. |
| Approach: | They propose a task of identifying moments of change in individuals on the basis of their shared content online. |
| Outcome: | The proposed task is based on 500 manually annotated user timelines and shows that it performs best through context aware sequential modelling. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)
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| Challenge: | Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings. |
| Approach: | They propose a framework for automating impression generation that exploits extra knowledge and original findings . they propose combining key words and their relations to extract critical information . |
| Outcome: | The proposed framework exploits extra knowledge and the original findings in an integrated way . the state-of-the-art results on two datasets confirm the effectiveness of the proposed method . |
Semi-Supervised Formality Style Transfer with Consistency Training (2022.acl-long)
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| Challenge: | Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. |
| Approach: | They propose a semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. |
| Outcome: | The proposed framework can achieve state-of-the-art results even with less than 40% of the parallel data. |
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure (2022.acl-long)
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| Challenge: | Multilingual pre-trained language models have shown impressive cross-lingual ability. |
| Approach: | They argue that cross-language ability comes from commonality between languages . they create an artificial language by modifying property in source language . |
| Outcome: | The proposed model can be implemented in multilingual and low-resource language scenarios without cross-lingual supervision or aligned data. |
Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting (2022.acl-long)
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| Challenge: | Word sense disambiguation (WSD) is a problem in the natural language processing community. |
| Approach: | They propose a method to adjust training on imbalanced word sense dataset . they propose to achieve performance gain on standard English all words benchmark . |
| Outcome: | The proposed method achieves performance gain on the standard English all words benchmark. |
Nibbling at the Hard Core of Word Sense Disambiguation (2022.acl-long)
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| Challenge: | Word Sense Disambiguation (WSD) is a task that is based on a set of pre-trained language models. |
| Approach: | They propose to use Word Sense Disambiguation to test whether systems can handle ambiguous words. |
| Outcome: | The proposed benchmarks show that seven of the most representative state-of-the-art systems make trivial errors on traditional evaluation benchmarks. |
Large Scale Substitution-based Word Sense Induction (2022.acl-long)
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| Challenge: | Word forms are ambiguous, and derive meaning from the context in which they appear . word sense induction can be performed over a corpus-derived sense inventory . |
| Approach: | They propose a word-sense induction method based on pre-trained masked language models . they train a static word embeddings algorithm on the sense-tagged corpus . |
| Outcome: | The proposed method outperforms existing senseful embeddings methods on Wikipedia and on an outlier detection dataset. |
Can Synthetic Translations Improve Bitext Quality? (2022.acl-long)
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| Challenge: | Synthetic translations have been used for a wide range of NLP tasks, but it remains unclear how they differ from naturally occurring data. |
| Approach: | They propose to use a semantic equivalence classifier to improve bitext quality without additional bilingual supervision to replace the originals. |
| Outcome: | The proposed samples improve bitext quality without additional bilingual supervision and are validated intrinsically and extrinsically through bilingual induction and MT tasks. |
Unsupervised Dependency Graph Network (2022.acl-long)
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| Challenge: | Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures. |
| Approach: | They propose a model that encourages attention heads to model different dependency relations from raw corpora and a masked language modeling task. |
| Outcome: | The proposed model can induce dependency structures from raw corpora and the masked language modeling task without gold POS tags and any external information. |
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)
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| Challenge: | Multimodal Entity Linking (MEL) is an essential task for many multimodal applications. |
| Approach: | They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models. |
| Outcome: | The proposed model uses the visual information of images more effectively than existing models. |
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models (2022.acl-long)
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| Challenge: | Despite the growing progress of probing knowledge for pre-trained language models, specialised areas such as the biomedical domain are vastly under-explored. |
| Approach: | They propose a biomedical knowledge probing benchmark, MedLAMA, constructed based on the Unified Medical Language System (UMLS) Metathesaurus. |
| Outcome: | The proposed approach pushes the acc@10 to 28%, but the performance gap remains notable. |
Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT (2022.acl-long)
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| Challenge: | Transformer-based pre-trained models achieve state-of-the-art results, but they can be prohibitively costly. |
| Approach: | They propose a fine- and coarse-granularity hybrid self-attention that shortens the computational sequence length in self- attention by progressively shortening the computational time. |
| Outcome: | The proposed model reduces computation cost by shortening the computational sequence length in self-attention. |
Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
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| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
| Outcome: | The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin. |
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration (2022.acl-long)
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| Challenge: | Existing methods of fine-tuning vision-language navigation models require extra human-labeled data and lack self-exploration capabilities in environments. |
| Approach: | They propose a method that can self-explore environments without human labeling . they use a large-scale cross-modal pretrained model to build an in-domain dataset . |
| Outcome: | The proposed model can self-explore environments without human labeling without human supervision and generates structured instructions without human intervention. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
Textomics: A Dataset for Genomics Data Summary Generation (2022.acl-long)
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| Challenge: | Summarizing biomedical discovery from genomics data is done manually but is slowing down the progress of scientific discovery. |
| Approach: | They propose a novel task of generating sentences to summarize a genomics data matrix using neural text generation. |
| Outcome: | The proposed model improves on the previous models and can be applied to other biomedical and natural language processing applications. |
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space (2022.acl-long)
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| Challenge: | Existing methods for learning sentence representations focus on constitution of positive and negative representation pairs and do not focus on training objective. |
| Approach: | They propose a new method to learn sentence representations using BERT-like pre-trained models . they use a pairwise discriminating power and a model to model the entailment relation of triplet sentences . |
| Outcome: | The proposed method outperforms the previous state-of-the-art on diverse sentence related tasks. |
Packed Levitated Marker for Entity and Relation Extraction (2022.acl-long)
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| Challenge: | Existing work on entity and relation extraction ignores the interrelation between spans . a novel approach to extract better span representations from pre-trained languages is needed . |
| Approach: | They propose a span representation approach that packs Levitated Markers to consider interrelation between spans. |
| Outcome: | The proposed model improves on baselines on six NER benchmarks and achieves a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models. |
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (2022.acl-long)
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| Challenge: | Existing approaches to interpret task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. |
| Approach: | They propose a neuro-symbolic approach that performs explicit reasoning that justifies model decisions by reasoning chains. |
| Outcome: | The proposed approach achieves better results and introduces an interpretable decision process. |
Impact of Evaluation Methodologies on Code Summarization (2022.acl-long)
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| Challenge: | Existing evaluation methodologies for code summarization tasks do not consider timestamps of code and comments. |
| Approach: | They propose a time-segmented evaluation methodology for code summarization that considers timestamps of code and comments during evaluation. |
| Outcome: | The proposed evaluation methodology compares with other evaluation methodologies that have been widely used. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
Which side are you on? Insider-Outsider classification in conspiracy-theoretic social media (2022.acl-long)
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| Challenge: | Social media is a breeding ground for threat narratives and related conspiracy theories. |
| Approach: | They propose a new insider-outsider classification task that leverages pretrained language modeling to classify insiders and outsiders. |
| Outcome: | The proposed model outperforms existing models and existing datasets in the classifying task. |
Learning From Failure: Data Capture in an Australian Aboriginal Community (2022.acl-long)
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| Challenge: | a prototype of a language data capture app for speakers was tested in an Aboriginal community . elicitation of word lists, phrases, etc. has been used for decades to collect data for Indigenous languages . many software tools are developed to support linguists' work . |
| Approach: | They propose to deploy an app for speakers to confirm system guesses in an approach to transcription based on word spotting. |
| Outcome: | The proposed app was tested in an Aboriginal community in australia . it was able to confirm system guesses without a transcription bottleneck . the results were compared with other apps in the community . |
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension (2022.acl-long)
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| Challenge: | a new method for multi-hop reading comprehension uses deep learning to reason across documents . a previous study showed that deep learning methods only implicitly encode query-related information into embeddings . |
| Approach: | They propose a deep-learning based inductive logic reasoning method that extracts query-related information and conducts logic reasoning among filtered information. |
| Outcome: | The proposed model is evaluated on two reading comprehension datasets . it uses attentive memories with novel differentiable logic operators . |
CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues (2022.acl-long)
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| Challenge: | Fig. 1a shows an example where commonsense knowledge is crucial in sifting relevant information from the context. |
| Approach: | They curate a dataset of dyadic conversations with five types of utterance-level reasoning-based inferences: cause, subsequent event, prerequisite, motivation, and emotional reaction. |
| Outcome: | The dataset contains 53,105 of such inferences from 5,672 dialogues. |
A Comparative Study of Faithfulness Metrics for Model Interpretability Methods (2022.acl-long)
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| Challenge: | Existing methods to reveal the reasoning processes of machine learning models are difficult to interpret due to their complexity. |
| Approach: | They propose to use diagnosticity and complexity to assess faithfulness of machine learning models . they propose to apply posthoc interpretation methods to reveal reasoning behind models based on internal reasoning . |
| Outcome: | The proposed interpretation metrics show conflicting preferences when comparing interpretations . sufficiency and comprehensiveness metrics have higher diagnosticity and lower complexity . |
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer (2022.acl-long)
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| Challenge: | Recent studies show that pre-trained language models can be more efficient when they are larger than they are in their size. |
| Approach: | They propose a prompt-based transfer learning approach called SPoT: Soft Prompt Transfer that learns a soft prompt on one or more source tasks and initializes it for a target task. |
| Outcome: | The proposed approach outperforms Prompt Tuning and MODELTUNING on superGLUE benchmarks while using up to 27,000 fewer task-specific parameters. |
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models (2022.acl-long)
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| Challenge: | Existing feature-based model selection methods are vulnerable to fine-tuning and are not reliable indicators for the PTM’s transferability. |
| Approach: | They propose feature-based model selection methods which assess PTMs’ transferability to a specific task in a fast way without fine-tuning. |
| Outcome: | The proposed methods can make FMS mistakenly judge transferability of models and can be combined with the backdoor attack to misguide the FMS to select poisoned models. |
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022.acl-long)
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| Challenge: | Existing methods to generate educational questions of fairytales or storybooks are difficult to implement due to adults lacking the skills or time to integrate such interactive opportunities. |
| Approach: | They propose a question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. |
| Outcome: | The proposed method performs well on automatic and human evaluation metrics on a newly proposed educational question-answering dataset FairytaleQA. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
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| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literature (2022.acl-long)
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| Challenge: | Existing evaluation approaches to multi-document summarization of biomedical literature lack consistency and transparency. |
| Approach: | They propose a systematic approach to human evaluation of biomedical summaries and apply it to analyze the summary generated by two current evaluation models. |
| Outcome: | The proposed evaluation framework is based on two state-of-the-art models and examines the summaries generated by the two models to understand the deficiencies of existing evaluation approaches. |
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (2022.acl-long)
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| Challenge: | Multi-document summarization models are limited by limited references and with maximum-likelihood objectives. |
| Approach: | They propose to fine-tune an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. |
| Outcome: | The proposed model improves on the Multi-News and WCEP datasets with a low-variance estimator . the proposed model also improves the coverage of the input documents . |
KNN-Contrastive Learning for Out-of-Domain Intent Classification (2022.acl-long)
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| Challenge: | Existing methods for OOD intent classification are limited to regions with compact or simply-connected features, which assumes no OOD intentions reside. |
| Approach: | They propose a method that uses k-nearest neighbors to learn discriminative semantic features that are more conducive to OOD detection. |
| Outcome: | The proposed method improves OOD detection performance while requiring no restrictions on feature distribution. |
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors (2022.acl-long)
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| Challenge: | Existing studies for understanding programs do not take human behaviors as reference. |
| Approach: | They propose a graph neural network model that takes human behaviors as reference in understanding programs. |
| Outcome: | The proposed model performs better on code summarization and code clone detection tasks. |
FaVIQ: FAct Verification from Information-seeking Questions (2022.acl-long)
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| Challenge: | Existing fact verification datasets with crowdsourced claims introduce subtle biases that are difficult to control for. |
| Approach: | They construct a large-scale fact verification dataset with ambiguous questions . they use a corpus of 188k claims to construct false and true claims . |
| Outcome: | The proposed dataset outperforms models trained on the dataset FEVER or in-domain data by up to 17% absolute. |
Simulating Bandit Learning from User Feedback for Extractive Question Answering (2022.acl-long)
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| Challenge: | Explicit feedback from users can be used to continually improve system performance. |
| Approach: | They study the potential of learning from user feedback for extractive question answering by simulating feedback using supervised data. |
| Outcome: | The proposed model improves on a few examples and can be deployed in new domains without any data annotation effort. |
Beyond Goldfish Memory: Long-Term Open-Domain Conversation (2022.acl-long)
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| Challenge: | Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. |
| Approach: | They propose to use retrieval-augmented methods to summarize and recall past conversations to improve their models. |
| Outcome: | The proposed models outperform the current state-of-the-art models on human-human chat sessions in both automatic and human evaluations. |
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2022.acl-long)
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| Challenge: | Visual referring expression comprehension (ReC) models can be trained for a domain, but it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC. |
| Approach: | They propose a method that repurposes CLIP, a state-of-the-art large-scale model, for training a referring expression comprehension model for a new visual domain. |
| Outcome: | The proposed model reduces the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on ReFGTA (video game imagery), and its relative improvement over supervised ReC models is 8%. |
Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)
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| Challenge: | Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005. |
| Approach: | They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context . |
| Outcome: | The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE. |
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models (2022.acl-long)
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| Challenge: | E-LANG is an efficient method for building large and highly capable language models . existing methods are only applicable to encoder-only backbones and classification tasks . |
| Approach: | They propose an efficient dynamic inference approach which distributes inference between large accurate Super-models and light-weight Swift models. |
| Outcome: | The proposed method outperforms existing methods on GLUE, SuperGLUE and WMT with 3.3X computation speed and 2.9X computation cost. |
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (2022.acl-long)
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| Challenge: | Existing pretrained models require domain-specific additional information to be effective. |
| Approach: | They propose a pre-trained model for multi-document representation with a focus on summarization that uses efficient encoder-decoder transformers to simplify the processing of concatenated input documents. |
| Outcome: | PRIMERA outperforms current state-of-the-art models on most datasets with large margins . PRImerA uses efficient encoder-decoder transformers to simplify processing of concatenated input documents. |
Dynamic Global Memory for Document-level Argument Extraction (2022.acl-long)
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| Challenge: | Recent work on document-level event argument extraction is restricted by sequence length constraints and ignores global context between events. |
| Approach: | They propose to construct a document memory store to extract contextual event information and leverage it to implicitly and explicitly help with decoding of arguments for later events. |
| Outcome: | The proposed framework outperforms prior methods and is more robust to adversarially annotated examples with constrained decoding design. |
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns (2022.acl-long)
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| Challenge: | Existing work to predict gaze patterns during naturalistic reading has not been conducted on general text characteristics. |
| Approach: | They propose to use two eye-tracking corpora of naturalistic reading and two language models to test their performance. |
| Outcome: | The proposed models predict eye-tracking measures during naturalistic reading and language processing. |
Alternative Input Signals Ease Transfer in Multilingual Machine Translation (2022.acl-long)
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| Challenge: | Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages. |
| Approach: | They propose to augment training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. |
| Outcome: | The proposed model outperforms strong ensemble baselines on Indic and Turkic languages by 1.3 BLEU points on both languages. |
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data (2022.acl-long)
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| Challenge: | Pre-trained language models are increasingly applied in ways that are agnostic to targeted downstream tasks. |
| Approach: | They propose a multi-modal approach to train language models using whatever text and/or audio data might be available in a language. |
| Outcome: | The proposed approach improves on pre-trained models on Swahili and Kinyarwanda data, with an improvement of up to 6% over models that are trained from scratch. |
Noisy Channel Language Model Prompting for Few-Shot Text Classification (2022.acl-long)
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| Challenge: | Prior work has suggested methods for finding better prompt or scoring of the output from the model. |
| Approach: | They propose a noisy channel approach for language model prompting in few-shot text classification by in-context demonstration or prompt tuning. |
| Outcome: | The proposed model outperforms direct models in both demonstration and prompt tuning. |
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages (2022.acl-long)
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| Challenge: | Unsupervised sequence segmentation is a key component of low-resource languages where there is little or no gold-standard data on which to train supervised models. |
| Approach: | They propose to pre-train a Masked Segmental Language Model multilingually to achieve unsupervised segmentation performance in extremely low-resource languages. |
| Outcome: | The proposed model outperforms a monolingual model and a pre-trained model on Quechua in 6/10 settings. |
KinyaBERT: a Morphology-aware Kinyarwanda Language Model (2022.acl-long)
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| Challenge: | Pre-trained language models such as BERT are sub-optimal at handling morphologically rich languages. |
| Approach: | They propose a two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphology in a low-resource Kinyarwanda language. |
| Outcome: | The proposed model outperforms baseline models on the low-resource morphologically rich Kinyarwanda language by 2% in F1 score and 4.3% in average score of GLUE benchmark. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
IMPLI: Investigating NLI Models’ Performance on Figurative Language (2022.acl-long)
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| Challenge: | Understanding figurative language is a difficult area in NLP but is essential for proper understanding. |
| Approach: | They propose to use a dataset to generate 24k semiautomatic pairs and manually create 1.8k gold pairs to evaluate NLI models. |
| Outcome: | The proposed models can detect entailment relationship between figurative phrases and their literal counterparts, but perform poorly on similar structured examples. |
QAConv: Question Answering on Informative Conversations (2022.acl-long)
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| Challenge: | Experimental results show that state-of-the-art pretrained QA systems have limited zero-shot performance and tend to predict our questions as unanswerable. |
| Approach: | They propose a question-answering dataset that uses conversations as a knowledge source. |
| Outcome: | The proposed dataset provides a training and evaluation testbed to facilitate QA on conversations research. |
Prix-LM: Pretraining for Multilingual Knowledge Base Construction (2022.acl-long)
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| Challenge: | Existing methods to build and enrich multilingual knowledge bases have not been successful . knowledge expressed in different languages may be complementary and unequally distributed . |
| Approach: | They propose a model that integrates useful multilingual and KB-based factual knowledge into a single model. |
| Outcome: | The proposed model can provide richer combined knowledge than monolingual KBs. |
Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations (2022.acl-long)
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| Challenge: | General graph-based meaning representations (MRs) that model sentence-level semantics aim to provide interpretable intermediate representations that are application-and domain-independent. |
| Approach: | They propose a probabilistic synchronous hyperedge replacement grammar for generating derivation trees from meaning representation graphs with a data-driven approach. |
| Outcome: | The proposed formalism approximates the semantic composition of DMRS graphs and recovers derivations that license them. |
HOLM: Hallucinating Objects with Language Models for Referring Expression Recognition in Partially-Observed Scenes (2022.acl-long)
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| Challenge: | a challenge in building AI systems physically present in the world is partial observability, a problem that exists when the entire state of the environment is not known or available to the system. |
| Approach: | They propose a method to infer object hallucinations for the unobserved part of the environment using large pre-trained language models. |
| Outcome: | The proposed method performs better than state-of-the-art approaches on two datasets for dRER. |
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (2022.acl-long)
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| Challenge: | Massively Multilingual Transformer based Language Models have been shown to be effective on zero-shot transfer across languages, though performance varies from language to language depending on pivot language(s) used for fine-tuning. |
| Approach: | They propose to combine multi-task learning problems with multi-lingual Transformers to model zero-shot transfer across languages. |
| Outcome: | The proposed model can predict zero-shot transfer across languages with a multi-task learning problem with pretraining data in very few languages. |
∞-former: Infinite Memory Transformer (2022.acl-long)
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| Challenge: | Several efficient transformers have been proposed, but they all have a finite memory capacity and are forced to drop old information. |
| Approach: | They propose an unbounded long-term memory extension that extends the vanilla transformer by using a continuous-space attention mechanism to attend over the long-time memory. |
| Outcome: | The proposed model can model arbitrarily long contexts while keeping the computation budget fixed. |
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)
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| Challenge: | Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages. |
| Approach: | They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
| Outcome: | The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
CaMEL: Case Marker Extraction without Labels (2022.acl-long)
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| Challenge: | Existing models for morphological case marking and semantic content are not isomorphic. |
| Approach: | They propose a model that extracts case markers from a multilingual corpus using a noun phrase chunker and an alignment system. |
| Outcome: | The proposed model can extract case markers in 83 languages and visualise similarities and differences between case systems and annotate fine-grained deep cases in languages where they are not overtly marked. |
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
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| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
| Approach: | They propose an interpretability technique to quantify the sensitivity of a trained model to new data . they propose a degree of explicitness metric to suggest out-of-domain unlabeled examples . |
| Outcome: | The proposed interpretability technique is useful for predicting the generalizability of the model on new data. |
Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)
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| Challenge: | Personal experiences and stories are important in argumentation, but they are not considered in the social sciences. |
| Approach: | They propose to use annotated documents to scale-up the analysis using existing annotations. |
| Outcome: | The proposed classifiers can identify documents containing personal experiences and reports . they can scale up to three domains and show that they perform well across domains. |
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022.acl-long)
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| Challenge: | In recent years, neural models have outperformed rule-based and classic approaches in NLG. |
| Approach: | They evaluate two English datasets and evaluate their performance using automatic and human evaluations. |
| Outcome: | The proposed model outperforms rule-based and classic approaches on two English datasets and is compared with human-based models. |
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion (2022.acl-long)
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| Challenge: | Existing text-to-SQL parsers struggle with out-of-domain generalization problems, arguing that they lack the ability to match domain specific phrases to composite operations over columns. |
| Approach: | They propose to use a synthetic dataset and a re-purposed train/test split to quantify out-of-domain generalization over column operations to address this problem. |
| Outcome: | The proposed method outperforms baseline parsers on the domain generalization problem, while boosting the underlying parser’ overall performance by 13.8% relative accuracy gain (5.1% absolute). |
Predicate-Argument Based Bi-Encoder for Paraphrase Identification (2022.acl-long)
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| Challenge: | Cross-encoders have been widely applied to sentence pair tasks . bi-encodings have significantly lower computation complexity and are better suited to symmetric tasks. |
| Approach: | They propose a bi-encoder approach to the paraphrase identification task and incorporate predicate-argument information into SBERT. |
| Outcome: | The proposed model outperforms SBERT/SRoBERTa on six paraphrase identification datasets with a minimal increase in parameters. |
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (2022.acl-long)
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Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang
| Challenge: | Named Entity Recognition models are feature-engineering and machine learning based. |
| Approach: | They propose a new NER learning framework that uses entity mentions to improve model performance. |
| Outcome: | The proposed model achieves better performance on OOV entities on various settings and datasets. |
Leveraging Wikipedia article evolution for promotional tone detection (2022.acl-long)
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| Challenge: | Detecting biased language is useful for a variety of applications, authors say . a dataset for document-level promotional tone detection is available for WikiEvolve . |
| Approach: | They propose a dataset for document-level promotional tone detection using Wikipedia . they use a gradient reversal framework to encode two versions simultaneously . |
| Outcome: | The proposed dataset improves on in-domain and out-of-domain evaluations. |
From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology (2022.acl-long)
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| Challenge: | Informal social interaction is the primordial home of human language. |
| Approach: | They show that linguistically diverse conversational corpora can provide empirical foundations for flexible, localizable language technologies of the future. |
| Outcome: | The results suggest that even relatively small corpora can support robust generalizations about key aspects of interactional infrastructure. |
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
| Approach: | They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. |
| Outcome: | The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. |
RoMe: A Robust Metric for Evaluating Natural Language Generation (2022.acl-long)
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| Challenge: | Empirical results suggest that RoMe has a stronger correlation to human judgment over state-of-the-art metrics in evaluating system-generated sentences across several NLG tasks. |
| Approach: | They propose an automatic evaluation metric incorporating several core aspects of natural language understanding (language competence, syntactic and semantic variation). |
| Outcome: | The proposed evaluation metric is trained on language features such as semantic similarity combined with tree edit distance and grammatical acceptability, using a self-supervised neural network. |
Finding Structural Knowledge in Multimodal-BERT (2022.acl-long)
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| Challenge: | Several multimodal-BERT models learn contextualized embeddings through training on linguistic data and visual data. |
| Approach: | They propose to make the structure of language and visuals explicit by a dependency parse . they also propose to encode the scene tree in the multimodal-BERT models . |
| Outcome: | The proposed models do not encode the scene trees in the language description. |
Fully Hyperbolic Neural Networks (2022.acl-long)
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| Challenge: | Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space. |
| Approach: | They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks. |
| Outcome: | The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models . |
Neural Machine Translation with Phrase-Level Universal Visual Representations (2022.acl-long)
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| Challenge: | Existing multimodal machine translation methods require paired input of source sentence and image, which makes them suffer from shortage of sentence-image pairs. |
| Approach: | They propose a phrase-level retrieval-based method to get visual information from existing sentence-image data sets. |
| Outcome: | The proposed method significantly outperforms strong baselines on multiple MMT datasets, especially when the textual context is limited. |
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)
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| Challenge: | Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity. |
| Approach: | They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series. |
| Outcome: | The proposed dataset contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. |
Few-shot Named Entity Recognition with Self-describing Networks (2022.acl-long)
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| Challenge: | Existing few-shot named entity recognition (NER) models capture information from limited instances while transferring useful knowledge from external resources. |
| Approach: | They propose a self-describing mechanism for few-shot NER which can universally describe mentions using concepts and automatically map novel entity types to concepts. |
| Outcome: | The proposed model can universally describe mentions using concepts and automatically map novel entity types to concepts and adaptively recognize entities on-demand. |
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)
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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
| Challenge: | Existing work shows that pre-trained models can improve in various natural language processing tasks. |
| Approach: | They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data. |
| Outcome: | The proposed framework is superior to existing models on speech-to-text processing tasks. |
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation (2022.acl-long)
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Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Damir Juric, Jack Flann, Ehud Reiter, Anya Belz, Aleksandar Savkov
| Challenge: | Recent studies suggest that note generation systems can be used to generate clinical consultation notes from the verbatim transcript of the consultation. |
| Approach: | They propose to use machine learning to generate consultation notes from the verbatim transcript of the consultation to evaluate their effectiveness. |
| Outcome: | The proposed model performs better than common model-based metrics like BertScore and is open-sourced. |
Unified Structure Generation for Universal Information Extraction (2022.acl-long)
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| Challenge: | Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. |
| Approach: | They propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. |
| Outcome: | The proposed framework can model different IE tasks, generate targeted structures, and learn general IE abilities from different knowledge sources. |
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)
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| Challenge: | Existing retrieval methods for knowledge base question answering are either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs. |
| Approach: | They propose a subgraph retrieval framework that decouples the retrieval from the subsequent reasoning process and trains subgraphs for easier reasoning. |
| Outcome: | The proposed framework improves retrieval and QA performance over existing methods. |
Pre-training to Match for Unified Low-shot Relation Extraction (2022.acl-long)
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| Challenge: | Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples. |
| Approach: | They propose a method that leverages triplet paraphrase to pre-train zero-shot label matching ability and uses meta-learning paradigm to learn few-shot instance summarizing ability. |
| Outcome: | The proposed method outperforms strong baselines and achieves the best performance on few-shot RE leaderboard. |
Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View (2022.acl-long)
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| Challenge: | Recent studies have found prompt-based probing evaluations inaccurate, inconsistent and unreliable. |
| Approach: | They propose to conduct debiasing via causal intervention to uncover biases in probing evaluations . authors argue that prompt-based probing is inaccurate, inconsistent and unreliable . |
| Outcome: | This paper examines the effectiveness of prompt-based probing in pretrained language models . it highlights critical biases which could induce biased results and conclusions . authors suggest rethinking criteria for evaluating better pretrained models based on such evaluations . |
Evaluating Extreme Hierarchical Multi-label Classification (2022.acl-long)
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| Challenge: | Several natural language processing tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification. |
| Approach: | They propose a classification metric inspired by the Information Contrast Model (ICM) they use a set of formal properties to analyze the evaluation metrics. |
| Outcome: | The proposed evaluation metrics are suitable for multi-label hierarchical extreme classification scenarios. |
What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels (2022.acl-long)
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| Challenge: | Existing systems can perform the first two tasks accurately, but attributing characters to direct speech is a challenging problem due to the narrator’s lack of explicit character mentions and the frequent use of nominal and pronominal coreference when such explicit mentions are made. |
| Approach: | They propose a pipeline to extract characters and link them to their direct-speech utterances by using a novel's list of characters and a list of attributing them to the speaking characters. |
| Outcome: | The proposed pipeline improves state-of-the-art models by 50% in F1-score compared with existing models . |
Measuring Fairness of Text Classifiers via Prediction Sensitivity (2022.acl-long)
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| Challenge: | Existing fairness metrics are not yet available to measure the fairness of language processing systems. |
| Approach: | They propose a new metric which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. |
| Outcome: | The proposed metric can be linked with a specific notion of group fairness and individual fairness, and correlates well with humans’ perception of fairness. |
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion (2022.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs can hardly model temporal relation patterns, lacking of interpretability. |
| Approach: | They propose a temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space and relations as complex vectors in Hamilton’s quaterniont space. |
| Outcome: | The proposed method can model key patterns of relations in TKG, such as symmetry, asymmetry, and inverse, and can capture time-evolved relations by theory. |
Feeding What You Need by Understanding What You Learned (2022.acl-long)
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| Challenge: | Existing research on machine reading comprehension rely heavily on large-size models and corpus to improve performance. |
| Approach: | They propose a framework that assesses model capabilities in an explainable and multi-dimensional manner. |
| Outcome: | The proposed framework achieves an 11.22% / 8.71% improvement of EM / F1 on MRC tasks. |
Probing Simile Knowledge from Pre-trained Language Models (2022.acl-long)
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Weijie Chen, Yongzhu Chang, Rongsheng Zhang, Jiashu Pu, Guandan Chen, Le Zhang, Yadong Xi, Yijiang Chen, Chang Su
| Challenge: | Existing approaches to learn generic knowledge from a large corpus are time-consuming and labor-intensive. |
| Approach: | They propose a framework to probe simile knowledge from pre-trained language models to solve SI and SG tasks. |
| Outcome: | The proposed framework solves the SI and SG tasks in a simile triple completion task. |
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism (2022.acl-long)
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| Challenge: | Existing methods focus on graph representation learning, but decoding is a key part of the process. |
| Approach: | They propose an EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI) they combine two sets of isomorphic equations to enhance the decoding process . |
| Outcome: | The proposed algorithm can deliver significant performance improvements even on the most advanced methods while the extra required time is less than 3 seconds. |
Entailment Graph Learning with Textual Entailment and Soft Transitivity (2022.acl-long)
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| Challenge: | Typed entailment graphs suffer from severe sparsity and unreliability of distributional similarity . enlargement relation is critical to semantic understanding and natural language inference . |
| Approach: | They propose a method to learn local entailment relations by recognizing textual enanglement between template sentences formed by typed CCG-parsed predicates. |
| Outcome: | The proposed method can model transitivity in entailment graphs to alleviate sparsity and improve performance over current methods. |
Logic Traps in Evaluating Attribution Scores (2022.acl-long)
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| Challenge: | Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models predict. |
| Approach: | They propose to review existing methods for evaluating attribution scores and summarize the logic traps in these methods. |
| Outcome: | The proposed methods show that they do not contain logic traps and that they are not reliable. |
Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022.acl-long)
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| Challenge: | a fundamental challenge in modeling math problems is how to fuse semantics of textual description and formulas. |
| Approach: | They propose a method to continually pre-train language models for improving understanding of math problems with syntax-aware memory networks. |
| Outcome: | The proposed approach outperforms competitive baselines on four math tasks. |
Multitasking Framework for Unsupervised Simple Definition Generation (2022.acl-long)
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| Challenge: | Existing definition generation tasks require a dictionary with complex definitions and a corpus containing arbitrary simple texts to generate them. |
| Approach: | They propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts. |
| Outcome: | The proposed framework outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%. |
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction (2022.acl-long)
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| Challenge: | Existing approaches to solve math word problems do not provide explanations for generated expressions. |
| Approach: | They propose a deductive approach that presents explainable deductive reasoning steps to iteratively construct target expressions. |
| Outcome: | The proposed model significantly outperforms existing strong baselines on four benchmark datasets. |
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues (2022.acl-long)
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| Challenge: | Indirect speech achieves a constellation of discourse goals in human communication, but it is challenging for AI agents to comprehend such idiosyncrasies. |
| Approach: | They propose a task to generate natural language explanations of satirical conversations using a multimodal and code-mixed dataset to capture multimodality. |
| Outcome: | The proposed task generates natural language explanations of satirical conversations in a multimodal and code-mixed setting and surpasses baselines on almost all metrics. |
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning (2022.acl-long)
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| Challenge: | Existing studies have shown that a pretrained language model can capture sentence similarity but there is no interpretation method for the sentence similarities. |
| Approach: | They propose a pretrained language model that captures sentence similarity between embeddings and a transport-based distance measure that leverages semantically-aligned token pairs. |
| Outcome: | The proposed framework outperforms baselines on both STS and interpretable-STS benchmarks and provides interpretation consistent with human judgement. |
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)
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| Challenge: | Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead. |
| Approach: | They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data. |
| Outcome: | The proposed approach outperforms current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs on three datasets and greatly reduces the huge size of training data. |
Multi-View Document Representation Learning for Open-Domain Dense Retrieval (2022.acl-long)
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| Challenge: | Existing methods for dense retrieval are hard to match with multiple views. |
| Approach: | They propose a multi-view document representation learning framework to generate multiple embeddings through viewers to represent documents and enforce them to align with different queries. |
| Outcome: | The proposed method outperforms recent works and achieves state-of-the-art results. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
| Outcome: | The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks. |
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills (2022.acl-long)
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| Challenge: | Large pre-trained language models struggle in tasks that require reasoning . recent work shows that they struggle in performing symbolic reasoning operations without substantial amounts of additional data. |
| Approach: | They propose to leverage semi-structured tables and generate at scale question-paragraph pairs where answering the question requires reasoning over multiple facts in the paragraph. |
| Outcome: | The proposed model outperforms T5, a popular pre-trained encoder-decoder model, on reasoning-focused reading comprehension datasets. |
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering (2022.acl-long)
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| Challenge: | Existing KBQA approaches struggle with generalization of unseen KB schema items . Rank-and-generate approach solves coverage issue with strong generalization . |
| Approach: | They propose a Rank-and-Generate approach for KBQA that uses a generation model to generalize to unseen KB schema items. |
| Outcome: | The proposed approach outperforms the prior state-of-the-art on GrailQA and WebQSP datasets. |
Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling (2022.acl-long)
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| Challenge: | Prior work on text generation models focused on new architectures for permuted document tasks. |
| Approach: | They propose to use a basic model architecture to improve coherence evaluation of machine generated text. |
| Outcome: | The proposed model improves on a task-independent test set and shows significant improvements in coherence evaluations of downstream tasks. |
Just Rank: Rethinking Evaluation with Word and Sentence Similarities (2022.acl-long)
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| Challenge: | Word and sentence similarity tasks are the de facto evaluation method for embeddings. |
| Approach: | They propose a new intrinsic evaluation method called EvalRank which shows a much stronger correlation with downstream tasks. |
| Outcome: | The proposed method shows a much stronger correlation with downstream tasks and is released for future benchmarking purposes. |
MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding (2022.acl-long)
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| Challenge: | Existing layout-based pre-training approaches are not easy to apply to VRDU tasks. |
| Approach: | They propose to use markup languages as the backbone for document understanding tasks where text and markup information are jointly pre-trained. |
| Outcome: | The proposed model outperforms existing models on document understanding tasks. |
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)
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| Challenge: | Previously, CLIP was only regarded as a powerful visual encoder. |
| Approach: | They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure. |
| Outcome: | The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
| Challenge: | Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale . |
| Approach: | They propose a compositional programming language to represent the reasoning process of complex questions. |
| Outcome: | The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model . |
Debiased Contrastive Learning of Unsupervised Sentence Representations (2022.acl-long)
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| Challenge: | Recent studies have shown that contrastive learning improves pre-trained language models to derive high-quality sentence representations. |
| Approach: | They propose a framework to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space. |
| Outcome: | The proposed framework improves pre-trained language models while pushing apart irrelevant negatives to guarantee the uniformity of the representation space. |
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators (2022.acl-long)
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| Challenge: | Prompting has been shown to be a promising approach for applying pre-trained language models to perform downstream tasks. |
| Approach: | They propose a method that divides the translation process into three stages using pre-trained language models. |
| Outcome: | The proposed method significantly improves translation performance of pre-trained language models on three translation tasks. |
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues (2022.acl-long)
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| Challenge: | Until now, researchers have separated open-domain and task-oriented dialogues into two different types due to their different purposes. |
| Approach: | They propose a framework to automatically generate many dialogues without human involvement . the framework can be easily leveraged to generate unlimited dialogues in target scenarios . |
| Outcome: | The proposed framework can automatically generate many dialogues without human involvement . the human evaluation shows that the generated dialogues have a reasonable quality . |
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining (2022.acl-long)
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| Challenge: | Existing phrase representation learning methods combine unigram representations in a context-free manner or rely on extensive annotations to learn context-aware knowledge. |
| Approach: | They propose a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining. |
| Outcome: | The proposed framework outperforms the state-of-the-art phrase representation model by 38.2% NMI on four entity clustering tasks. |
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)
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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei
| Challenge: | ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive . |
| Approach: | They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora . |
| Outcome: | The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost. |
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing (2022.acl-long)
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| Challenge: | Existing methods to recognize named entities have been criticized for their performance on flat NER but fail to handle nested entities. |
| Approach: | They propose to use a span-based constituency parser to tackle nested NER . they use lexicalized constituency trees to model nesting entities . |
| Outcome: | The proposed method achieves state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive performance on the GENIA platform. |
Can Explanations Be Useful for Calibrating Black Box Models? (2022.acl-long)
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| Challenge: | Existing models are often used as black boxes to adapt to new domains, but there is no single recipe for making them work. |
| Approach: | They propose to use black box models to improve their performance on new domains by leveraging explanations of their behavior. |
| Outcome: | The proposed method improves model generalization performance on two tasks using explanations. |
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)
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| Challenge: | Different Open Information Extraction (OIE) tasks require different types of information. |
| Approach: | They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible . |
| Outcome: | The proposed system achieves new SOTA performance on three popular OIE tasks. |
ReACC: A Retrieval-Augmented Code Completion Framework (2022.acl-long)
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| Challenge: | Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks. |
| Approach: | They propose a retrieval-augmented code completion framework that combines a source code retriever and an auto-regressive language model for programming language. |
| Outcome: | The proposed framework achieves state-of-the-art on CodeXGLUE benchmark. |
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED (2022.acl-long)
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| Challenge: | Document-level relation extraction is a challenging task as it requires reasoning across multiple sentences. |
| Approach: | They propose to use a recommend-revise scheme to reduce the workload of annotators by providing them with candidate relation instances from distant supervision to supplement and remove relational facts. |
| Outcome: | The proposed dataset is the first large-scale and human-annotated dataset for relation extraction. |
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning (2022.acl-long)
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| Challenge: | Existing methods for parameter-efficient language model tuning (PELT) match the performance of fine-tuning with fewer trainable parameters. |
| Approach: | They propose a framework which integrates different PELT methods as submodules and learns to activate the ones that best suit the current data or task setup via gating mechanism. |
| Outcome: | The proposed framework outperforms fine-tuning methods on the GLUE benchmark and achieves 14% gains over the best individual PELT method. |
An Empirical Study of Memorization in NLP (2022.acl-long)
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| Challenge: | Existing studies see memorization as hindering generalization in deep learning models. |
| Approach: | They propose a long-tail theory to explain the memorization behavior of deep learning models . they use three different NLP tasks to test whether the theory holds . |
| Outcome: | The proposed long-tail theory is validated in three NLP tasks and shows it is faithful. |
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages (2022.acl-long)
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Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann
| Challenge: | Pretrained multilingual models can perform cross-lingual transfer in a zero-shot setting, even for unseen languages. |
| Approach: | They propose to extend XNLI to 10 indigenous languages of the Americas and test multiple zero-shot and translation-based approaches. |
| Outcome: | The proposed model can perform cross-lingual transfer in a zero-shot setting even for languages unseen during pretraining. |
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts (2022.acl-long)
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| Challenge: | Existing models that focus on identifying functional (dis)similarity of source code get confused when trying to identify functional (Dis)-similarities. |
| Approach: | They propose to pre-train a Transformer model with such automatically generated program contrasts to better identify similar code in the wild and differentiate vulnerable programs from benign ones. |
| Outcome: | The proposed model outperforms existing models in vulnerability and code clone detection tasks even with much less data. |
Guided Attention Multimodal Multitask Financial Forecasting with Inter-Company Relationships and Global and Local News (2022.acl-long)
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| Challenge: | Stock returns in financial markets are influenced by textual information from diverse sources. |
| Approach: | They propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models in several forecasting tasks and important real-world applications. |
On Vision Features in Multimodal Machine Translation (2022.acl-long)
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| Challenge: | Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models. |
| Approach: | They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation. |
| Outcome: | The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models. |
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning (2022.acl-long)
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| Challenge: | Existing methods for Named Entity Recognition only learn class-specific semantic features and intermediate representations from source domains, resulting in suboptimal performance. |
| Approach: | They propose a contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. |
| Outcome: | The proposed technique outperforms existing methods by 3%-13% absolute F1 points while showing consistent performance trends. |
Cree Corpus: A Collection of nêhiyawêwin Resources (2022.acl-long)
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| Challenge: | Plains Cree is a low resource language with no corpus available for development . a lack of publicly available corpora hinders the development of such technologies . |
| Approach: | They develop a corpus of Plains Cree (nêhiyawêwin) covering genres, time periods, and texts for a variety of intended audiences. |
| Outcome: | The corpus covers genres, time periods, and texts for a variety of intended audiences. |
Learning to Rank Visual Stories From Human Ranking Data (2022.acl-long)
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| Challenge: | Existing studies on visual storytelling (VIST) use automated evaluation metrics for text generation. |
| Approach: | They develop a Vrank metric that repurposes human evaluation results for automatic evaluation. |
| Outcome: | The proposed model is more accurate than existing metrics and is generalizable to textual stories. |
Universal Conditional Masked Language Pre-training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT) this paper demonstrates that pre-training a sequence- to-squence model with a bidirectional decoder can produce notable performance gains for both Autoregressive and Non-autoregressive NMT tasks. |
| Approach: | They propose a conditional masked language model pre-trained on bilingual and monolingual corpora in many languages. |
| Outcome: | The proposed model can achieve significant performance improvements on all scenarios from low- to extremely high-resource languages. |
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA (2022.acl-long)
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| Challenge: | Existing VQA tests lack superficial correlations and other weaknesses, which lead to optimistic evaluations when considering accuracy alone. |
| Approach: | They introduce a system to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. |
| Outcome: | The proposed test suite evaluates six modern VQA systems and identifies several actionable weaknesses in model comprehension. |
Phrase-aware Unsupervised Constituency Parsing (2022.acl-long)
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| Challenge: | Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task. |
| Approach: | They propose to regularize the parser with phrases extracted by an unsupervised phrase tagger to help the LM model quickly manage low-level structures. |
| Outcome: | The proposed method improves the identification of high-level structures using phrase-guided masking. |
Achieving Reliable Human Assessment of Open-Domain Dialogue Systems (2022.acl-long)
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| Challenge: | Evaluation of open-domain dialogue systems is challenging and unreliable . human evaluation of live conversations is highly reliable, but reliability cannot be assumed . |
| Approach: | They propose a method of open-domain dialogue evaluation that is highly reliable . they compare live conversations with models that avoid pre-created reference dialogues . |
| Outcome: | The proposed method is highly reliable while remaining feasible and low cost. |
Updated Headline Generation: Creating Updated Summaries for Evolving News Stories (2022.acl-long)
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| Challenge: | Existing systems that generate headlines for updated articles are not as efficient as static ones. |
| Approach: | They propose a task where a system generates a headline for an updated article, considering both the previous article and headline. |
| Outcome: | The proposed model produces headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. |
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures (2022.acl-long)
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| Challenge: | Existing open-domain conversational models can easily be made to talk in inadequate ways. |
| Approach: | They propose a task and dataset of graceful responses to safety feedback . they collect 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging feedback. |
| Outcome: | The proposed model improves on a dataset of 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback. |
Compositional Generalization in Dependency Parsing (2022.acl-long)
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| Challenge: | Compositionality is the ability to combine familiar units like words into novel phrases and sentences. |
| Approach: | They introduce a set of dependency parses for Compositional Freebase Queries (CFQ) they analyze the behaviour of a state-of-the-art dependency parser on the CFQ dataset . |
| Outcome: | The proposed dependency parser performs lower on the most challenging splits with the highest compound divergence. |
ASPECTNEWS: Aspect-Oriented Summarization of News Documents (2022.acl-long)
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| Challenge: | Existing methods for generating generic summarizations can't be used to generalize to these domains without seeing in-domain training data. |
| Approach: | They use a dataset of real-world aspect-oriented summaries to annotate articles from two different news sub-domains. |
| Outcome: | The proposed approach produces better focused summaries than existing systems without seeing in-domain training data. |
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes (2022.acl-long)
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| Challenge: | MemSum is a reinforcement-learning-based extractive summarizer that considers the text content of the sentence, the global context of the rest of the document, and the extraction history of the sentences that have already been extracted. |
| Approach: | They propose a reinforcement-learning-based extractive summarizer that iteratively selects sentences from a broad set of information that would intuitively be used by humans. |
| Outcome: | The proposed extractive summarizer is enriched with information on the extraction history and local, global, and historical information. |
CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations (2022.acl-long)
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| Challenge: | Supervised learning has traditionally focused on inductive learning by looking at labeled examples of a task. |
| Approach: | They propose a benchmark for Classifier Learning Using natural language ExplanationS that provides natural language supervision over structured data and entailment-based models that learn from explanations. |
| Outcome: | The proposed model generalizes 18% better (relative) on novel tasks than a baseline that does not use explanations. |
Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing (2022.acl-long)
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| Challenge: | Substructure distribution projection (SubDP) projects a distribution over structures in one domain to another, using substructure distributions as silver labels. |
| Approach: | They evaluate substructure distribution projection technique on zero shot cross-lingual dependency parsing using dependency arcs as substructures. |
| Outcome: | The proposed technique achieves better unlabeled attachment score than previous work on eight target languages and best labeled attachment scores on six languages. |
Multilingual Detection of Personal Employment Status on Twitter (2022.acl-long)
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| Challenge: | Detecting disclosures of individuals’ employment status on social media is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. |
| Approach: | They propose to use BERT-based classification models to identify five types of disclosures about individuals’ employment status in three languages. |
| Outcome: | The proposed methods achieve significant gains in precision, recall, and diversity of results in real-world settings of extreme class imbalance. |
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data (2022.acl-long)
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| Challenge: | Existing benchmarks for numerical reasoning over hybrid data only include a single flat table in each document . |
| Approach: | They propose a new benchmark with QA pairs over multi hierarchical tabular and textual data. |
| Outcome: | The proposed model is more complex and challenging than existing benchmarks and is available on github . it uses facts retrieving to extract relevant facts from both tables and text and symbolic reasoning over retrieved facts. |
Transformers in the loop: Polarity in neural models of language (2022.acl-long)
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| Challenge: | Recent Transformer-based language representation models (LRMs) show impressive results on practical text analysis tasks, but do they have access to complex linguistic notions? |
| Approach: | They propose to use polarity as a case study to compare metrics derived from language models to human judgments obtained in psycholinguistic experiments. |
| Outcome: | The proposed model is more accurate than linguistic theory predictions for polarity, and allows us to use language models to discover new insights into natural language grammar beyond existing linguistic theories. |
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation (2022.acl-long)
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| Challenge: | Experimental results show that backtranslation improves UNMT performance by reducing the data gap between training and inference. |
| Approach: | They propose an online method to remedy the source discrepancy between training and inference . they use pseudo parallel data with translated source and translated target to mimic inference scenario . |
| Outcome: | The proposed method outperforms baselines on several widely-used language pairs by remedying the style and content gaps. |
SDR: Efficient Neural Re-ranking using Succinct Document Representation (2022.acl-long)
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| Challenge: | BERT based ranking models have been successful on various information retrieval tasks, but they are prone to storage and network fetching latency. |
| Approach: | They propose a late-interaction architecture that allows pre-computation of intermediate document representations, thus reducing latency. |
| Outcome: | The proposed model achieves 4x–11.6x higher compression rates on the MSMARCO passage re-reranking task compared to existing methods. |
The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications (2022.acl-long)
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| Challenge: | Task-oriented dialogue systems have been surveyed in the medical community from a non-technical perspective, but a systematic review from . a rigorous computational perspective has to date remained noticeably absent. |
| Approach: | They analyze 4070 papers on task-oriented dialogue systems for healthcare applications and identify gaps in their analysis. |
| Outcome: | The proposed system-level implementation details remain limited or underspecified, slowing the pace of innovation in this area. |
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher (2022.acl-long)
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| Challenge: | Existing methods to defend textual neural network models against adversarial attacks often require retraining and retrain . e.g., BERT, RoBERTa require great time and computation resources. |
| Approach: | They propose an algorithm that modifies and re-trains only the last layer of a textual NN and transforms it into a stochastic weighted ensemble of multi-expert prediction heads. |
| Outcome: | The proposed algorithm outperforms existing models against black-box attacks by 15%–70% . the proposed algorithm is based on a novel algorithm from software engineering . |
Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding (2022.acl-long)
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| Challenge: | Existing methods of offline alignment use only the entire target sentence. |
| Approach: | They propose a posterior alignment technique that is truly online in its execution and superior in terms of alignment error rates compared to existing methods. |
| Outcome: | The proposed technique is online in execution and superior in alignment error rates compared to existing methods. |
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data (2022.acl-long)
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| Challenge: | Existing text classification systems require thousands of in-domain text data to achieve high performance. |
| Approach: | They propose an algorithm to improve task transferability of meta-learning-based text classification by normalizing negative conditional entropy from source task data to boost cross-domain meta- learning accuracy. |
| Outcome: | The proposed method improves section classification accuracy significantly compared to meta-learning algorithms. |
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection (2022.acl-long)
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| Challenge: | Existing ‘Stereotype Detection’ datasets adopt a diagnostic approach toward large PLMs. |
| Approach: | They propose a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance. |
| Outcome: | The proposed model achieves significant gains over baselines on hate speech detection, offensive language detection, misogyny detection, etc. |
Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings (2022.acl-long)
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| Challenge: | a great deal of work has been done on NLP approaches to lexical semantic change detection, but other aspects of language change have received less attention from the NLP community. |
| Approach: | They propose to compare the relative distance through time between the distributions of the characters involved before and after a sound change has taken place. |
| Outcome: | The proposed method can trace the well-known historical change of lenition of plosives in Danish historical sources and identify several of the changes under consideration and uncover meaningful contexts in which they appeared. |
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation (2022.acl-long)
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| Challenge: | Existing work on pre-trained generative models often fails to detect non-existent or incorrect content . Existing studies have attempted to detect hallucinations based on oracle references . |
| Approach: | They propose a token-level, reference-free hallucination detection task based on Wikipedia annotations to detect non-existent or incorrect content. |
| Outcome: | The proposed task is token-level, reference-free hallucination detection task and dataset . authors argue that the proposed task can be used in real-time to detect hallucines . |
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice (2022.acl-long)
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| Challenge: | Probabilistic multiclass classifiers with large number of output classes are commonplace in natural language processing. |
| Approach: | They propose to use argmax to predict words from a large vocabulary in NLP models . they find that 13 out of 150 models do indeed have such unargmaxable tokens . |
| Outcome: | The proposed algorithms detect unargmaxable tokens in large language models and translation models. |
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2022.acl-long)
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| Challenge: | Using a prompt-based model, we find that event argument extraction is efficient and generalized well to few-shot settings. |
| Approach: | They propose a model PAIE for event argument extraction using prompt tuning for extractive objectives. |
| Outcome: | The proposed model can extract arguments with the same role instead of heuristic threshold tuning. |
Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework (2022.acl-long)
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| Challenge: | Existing methods for simultaneous machine translation (SiMT) are more challenging since the source sentence is always incomplete during translating. |
| Approach: | They propose a framework to reduce the position bias by bridging the structural gap between SiMT and full-sentence MT. |
| Outcome: | The proposed framework reduces the position bias by bridging the structural gap between SiMT and full-sentence MT. |
A Statutory Article Retrieval Dataset in French (2022.acl-long)
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| Challenge: | Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question. |
| Approach: | They propose to use a Belgian Statutory Article Retrieval Dataset to test various retrieval approaches including lexical and dense architectures to achieve a 74.8% R@100. |
| Outcome: | The proposed dataset outperforms existing systems in both zero-shot and supervised setups. |
ParaDetox: Detoxification with Parallel Data (2022.acl-long)
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Varvara Logacheva, Daryna Dementieva, Sergey Ustyantsev, Daniil Moskovskiy, David Dale, Irina Krotova, Nikita Semenov, Alexander Panchenko
| Challenge: | a pipeline for the collection of parallel data for the detoxification task is available. |
| Approach: | They propose a pipeline for the collection of parallel data for the detoxification task . they collect non-toxic paraphrases for over 10,000 English toxic sentences . |
| Outcome: | The proposed pipeline outperforms state-of-the-art models on both automatic and manual evaluations. |
Interpreting Character Embeddings With Perceptual Representations: The Case of Shape, Sound, and Color (2022.acl-long)
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| Challenge: | Character-level information is included in many NLP models, but evaluating the information encoded in character embeddings is an open issue. |
| Approach: | They propose to use shape, sound, and color embeddings to evaluate the information encoded in character representations in five languages to perform cross-lingual analysis. |
| Outcome: | The proposed classifiers evaluate phonological information encoded in character embeddings and LSTM models. |
Fine-Grained Controllable Text Generation Using Non-Residual Prompting (2022.acl-long)
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| Challenge: | Existing approaches to control the text generation process are not expressive enough. |
| Approach: | They propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. |
| Outcome: | The proposed architecture is expressive and versatile on multiple experimental settings. |
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features (2022.acl-long)
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| Challenge: | Recent advances in text-to-speech systems allow for speech synthesis with unprecedented quality and controllability. |
| Approach: | They use embeddings derived from articulatory vectors rather than phoneme identities to learn phoneme representations that hold across languages. |
| Outcome: | The proposed models fine-tuned on 30 minutes of data in a previously unseen language with language agnostic meta learning. |
TwittIrish: A Universal Dependencies Treebank of Tweets in Modern Irish (2022.acl-long)
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| Challenge: | Modern Irish is a minority language lacking computational resources for accurate automatic syntactic parsing of user-generated content. |
| Approach: | They propose to use a treebank to facilitate natural language parsing of user-generated content in Irish. |
| Outcome: | The proposed treebank enables natural language processing of user-generated content in Irish. |
Length Control in Abstractive Summarization by Pretraining Information Selection (2022.acl-long)
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| Challenge: | Existing length-controllable summarization models generate summaries as long as training data . current methods only control lengths at decoding stage, but adapt to desired lengths . |
| Approach: | They propose a length-aware attention mechanism to adapt the encoding of the source based on the desired length. |
| Outcome: | The proposed method produces high-quality summaries with desired lengths and even those short lengths never seen in the training data. |
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)
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| Challenge: | Current models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning. |
| Approach: | They propose a controlled framework to generate multi-hop questions that contain key entities in multi- hop reasoning chains and a novel Transformer-based decoder to guarantee that key entities appear in the questions. |
| Outcome: | The proposed model outperforms the state-of-the-art model 25% on HotpotQA. |
Word Order Does Matter and Shuffled Language Models Know It (2022.acl-long)
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| Challenge: | Recent studies have shown that language models pretrained and/or fine-tuned on randomly permuted sentences exhibit competitive performance on GLUE, putting into question the importance of word order information. |
| Approach: | They propose a transformer-based BERT architecture that uses a fixed, sinusoidal position embedding added to each token embeddable to compensate for this absence of linear order. |
| Outcome: | The proposed model retains word order information because of the dependencies between sentence length and unigram probabilities. |
An Empirical Study on Explanations in Out-of-Domain Settings (2022.acl-long)
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| Challenge: | Recent work in Natural Language Processing has focused on extracting faithful explanations . yet, little is known about how post-hoc explanations perform in out-of-domain settings . |
| Approach: | They propose to use a random baseline to evaluate out-of-domain post-hoc explanation faithfulness . they suggest select-then-predict models demonstrate comparable predictive performance in out- of-domain settings to full-text trained models. |
| Outcome: | The proposed models perform better in out-of-domain settings than full-text models. |
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)
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Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
| Challenge: | Current OpenIE systems extract all triple slots independently. |
| Approach: | They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones . |
| Outcome: | The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. |
What Makes Reading Comprehension Questions Difficult? (2022.acl-long)
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| Challenge: | a recent study shows that natural language understanding benchmarks are not able to measure future progress . a crowdsourcing approach is needed to collect diverse examples without sacrificing diversity or coverage. |
| Approach: | They crowdsource multiple-choice reading comprehension questions for passages from seven sources . they find passage source, length, and readability measures do not significantly affect question difficulty . |
| Outcome: | The results show that passage source, length, and readability measures do not significantly affect question difficulty. |
From Simultaneous to Streaming Machine Translation by Leveraging Streaming History (2022.acl-long)
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| Challenge: | Streaming MT is an extension of simultaneous MT to the incremental translation of a continuous input text stream. |
| Approach: | They propose to extend simultaneous machine translation to streaming setups by leveraging streaming history. |
| Outcome: | The proposed system compares favorably to the best performing systems on IWSLT Translation Tasks. |
A Rationale-Centric Framework for Human-in-the-loop Machine Learning (2022.acl-long)
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| Challenge: | Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-district tests, especially for few-shot learning scenarios. |
| Approach: | They propose a rational-centric framework with human-in-the-loop to exploit spurious associations and bias models towards generally applicable underlying distributions. |
| Outcome: | The proposed framework leads to significant prediction benefits on in-distribution and out-of-district tests, compared to state-of the-art benchmarks. |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
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Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
Prototypical Verbalizer for Prompt-based Few-shot Tuning (2022.acl-long)
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| Challenge: | Prompt-based tuning for pre-trained language models has shown its effectiveness in few-shot learning. |
| Approach: | They propose a prototypical verbalizer which learns prototype vectors as verbalizes by contrastive learning. |
| Outcome: | The proposed verbalizer outperforms existing verbalizing methods on topic classification and entity typing tasks. |
Clickbait Spoiling via Question Answering and Passage Retrieval (2022.acl-long)
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| Challenge: | Clickbait is a term used to describe posts intended to entice readers to visit a web page . clickbait spoiling is generating a short text that satisfies the curiosity induced by a clickbaiting post . |
| Approach: | They propose to use clickbait spoiling to generate a short text that satisfies curiosity . they classify the type of spoiler needed and generate appropriate spoilers . |
| Outcome: | The proposed method outperforms all other methods in generating spoilers for both types of clickbait posts. |
BERT Learns to Teach: Knowledge Distillation with Meta Learning (2022.acl-long)
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| Challenge: | Existing knowledge distillation methods are based on teacher model, but have drawbacks . a teacher model is fixed during training, but meta learning can improve student performance . |
| Approach: | They propose a meta learning framework that allows the teacher network to learn to better transfer knowledge to the student network. |
| Outcome: | Experiments show that MetaDistil can improve on existing methods and is less sensitive to student capacity and hyperparameters. |
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation (2022.acl-long)
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| Challenge: | Existing methods to learn speech representations for end-to-end speech-totext translation (ST) neglect the representation discrepancy across modalities. |
| Approach: | They propose a method to calibrate the representation discrepancy between modalities by mixing up the representation sequences of different modality inputs. |
| Outcome: | The proposed method alleviates the cross-modal representation discrepancy and improves on a strong baseline on eight translation directions. |
Integrating Vectorized Lexical Constraints for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on integrating discrete lexical constraints into neural machine translation models. |
| Approach: | They propose to integrate constraints into NMT models by integrating them into keys and values . they show that their method outperforms representative baselines on four language pairs . |
| Outcome: | The proposed method outperforms baselines on four language pairs, showing superiority . |
MPII: Multi-Level Mutual Promotion for Inference and Interpretation (2022.acl-long)
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| Challenge: | Existing methods for providing interpretations provide human-unfriendly interpretations, resulting in sub-optimal performance. |
| Approach: | They propose a multi-level Mutual Promotion mechanism for self-evolved inference and sentence-level interpretation that integrates inference with interpretation in an autoregressive manner. |
| Outcome: | The proposed approach outperforms baseline models on NLI and CQA tasks for both inference performance and interpretation quality. |
StableMoE: Stable Routing Strategy for Mixture of Experts (2022.acl-long)
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| Challenge: | Existing learning-to-route methods suffer from the routing fluctuation issue . with the model scale growing, training speed will go slower and memory requirements are heavy . |
| Approach: | They propose a Mixture-of-Experts technique that can scale up the model size of Transformers with an affordable computational overhead. |
| Outcome: | The proposed method outperforms existing learning-to-route methods on language modeling and multilingual machine translation. |
Boundary Smoothing for Named Entity Recognition (2022.acl-long)
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| Challenge: | Named entity recognition models often encounter over-confidence issues . boundary smoothing is a method that re-assigns entity probabilities from annotated spans to the surrounding ones . |
| Approach: | They propose a method for regularizing entity probabilities from annotated spans to the surrounding ones. |
| Outcome: | The proposed method achieves better than or competitive with previous state-of-the-art systems on well-known benchmarks. |
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification (2022.acl-long)
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| Challenge: | Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text. |
| Approach: | They propose to embed hierarchy into a text encoder by combining input and output data to generate a hierarchy-aware representation. |
| Outcome: | Extensive experiments on three benchmark datasets verify the effectiveness of the proposed model. |
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models (2022.acl-long)
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Mark Chu, Bhargav Srinivasa Desikan, Ethan Nadler, Donald Ruggiero Lo Sardo, Elise Darragh-Ford, Douglas Guilbeault
| Challenge: | Existing words represent an extremely small fraction of the space of possible character level n-grams (word forms) yet, a plethora of insights into language learning have emerged from inquiries into language beyond extant words, such as the grammatical errors and inference patterns children exhibit when distinguishing extant word from non-linguistic auditory signals. |
| Approach: | They propose that random character n-grams provide a novel context for studying word meaning both within and beyond extant language. |
| Outcome: | The proposed model identifies an axis in its high-dimensional embedding space that separates these classes of n-grams from other classes of characters and relates to structure within extant language, including word part-of-speech, morphology, and concept concreteness. |
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)
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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen
| Challenge: | Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance. |
| Approach: | They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents. |
| Outcome: | The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain. |
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension (2022.acl-long)
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| Challenge: | Existing methods and limitations for machine reading comprehension are insufficient for logical reasoning over text. |
| Approach: | They propose a neural-symbolic approach which passes messages over a graph representing logical relations between text units to predict an answer. |
| Outcome: | The proposed approach outperforms existing methods on ReClor and LogiQA. |
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing (2022.acl-long)
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| Challenge: | Existing work has resorted to sharing weights among models, but results are not affordable for real-world deployment. |
| Approach: | They propose a consistency-regularized ensemble learning approach based on perturbed models to retain ensemble benefits while maintaining a low memory cost. |
| Outcome: | The proposed approach outperforms the standard ensemble of 8 BERT-base models on the GLUE benchmark by 0.7 with a significantly smaller model size. |
Interpretability for Language Learners Using Example-Based Grammatical Error Correction (2022.acl-long)
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| Challenge: | Existing neural-based GEC models mainly aim at improving accuracy, but their interpretability has not been explored. |
| Approach: | They propose an example-based method that generates corrections using retrieved examples. |
| Outcome: | The proposed method improves interpretability and supports language learners. |
Rethinking Negative Sampling for Handling Missing Entity Annotations (2022.acl-long)
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| Challenge: | Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling. |
| Approach: | They propose an adaptive and weighted sampling distribution that further improves negative sampling by introducing missampling and uncertainty concepts. |
| Outcome: | The proposed approach improves on synthetic and well-annotated datasets in terms of F1 score and loss convergence. |
Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning (2022.acl-long)
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| Challenge: | Existing methods for named entity recognition suffer from incomplete annotations due to incompleteness of external knowledge bases. |
| Approach: | They propose a method to solve the named entity recognition problem under distant supervision using dictionaries and knowledge bases. |
| Outcome: | The proposed method outperforms existing methods on two benchmark datasets labeled by various knowledge bases. |
UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)
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| Challenge: | Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks . |
| Approach: | They propose a unified cross-modal pre-trained model for programming language that leverages cross-module contents like AST and code comment to enhance code representation. |
| Outcome: | The proposed model achieves state-of-the-art on most code-related tasks and compares with existing models on zero-shot code-to-code search. |
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia (2022.acl-long)
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Alham Fikri Aji, Genta Indra Winata, Fajri Koto, Samuel Cahyawijaya, Ade Romadhony, Rahmad Mahendra, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Timothy Baldwin, Jey Han Lau, Sebastian Ruder
| Challenge: | There are more than 700 languages spoken in Indonesia, equal to 10% of the world's languages, second only to Papua New Guinea. |
| Approach: | They focus on the languages spoken in Indonesia, the world's second most linguistically diverse nation, and the fourth most populous nation of the world. |
| Outcome: | The proposed model is based on the languages spoken in Indonesia, the world's second-most linguistically diverse nation, with 273 million people spread over 17,508 islands. |
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text (2022.acl-long)
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| Challenge: | a recent study has reported that crowdsourcing cannot distinguish between machine-authored and human-authored text. |
| Approach: | They propose a framework called Scarecrow for scrutinizing machine text via crowd annotation . they use crowd annotation to identify redundancy, commonsense errors, and incoherence . |
| Outcome: | The proposed method quantifies gaps between human-authored and machine-generated text . it can detect redundancy, commonsense errors, and incoherence . |
Transkimmer: Transformer Learns to Layer-wise Skim (2022.acl-long)
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| Challenge: | Prior work has proposed to augment Transformer model with the capability of skimming tokens to improve its computational efficiency. |
| Approach: | They propose to add a parameterized predictor before each layer that learns to make the skimming decision. |
| Outcome: | The proposed model achieves 10.97x speedup on GLUE benchmark compared with BERT-base baseline with less than 1% accuracy degradation. |
SkipBERT: Efficient Inference with Shallow Layer Skipping (2022.acl-long)
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| Challenge: | Pre-trained language models have significant demands in computation and inference time, limiting their use in resource-constrained or latencysensitive applications. |
| Approach: | They propose to encode text chunks into independent representations and skip computation of shallow layers to accelerate inference. |
| Outcome: | The proposed approach can reduce latency by 65% without sacrificing performance. |
Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models (2022.acl-long)
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| Challenge: | Existing studies show that pretraining with an artificial language with nesting dependency structure provides some knowledge transferable to natural language. |
| Approach: | They propose to pretrain artificial languages with structural properties that mimic natural language and then test their performance on downstream tasks. |
| Outcome: | The proposed language models show strong performance across languages and languages. |
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models (2022.acl-long)
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| Challenge: | Existing methods for improving multilingual models only use entity information in pretraining and do not explicitly use entities in downstream tasks. |
| Approach: | They propose to leverage Wikipedia entity representations for downstream tasks . they train a multilingual language model with 24 languages with entity representation . |
| Outcome: | The proposed model outperforms word-based models in cross-lingual transfer tasks. |
Evaluating Factuality in Text Simplification (2022.acl-long)
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| Challenge: | Automated simplification models aim to make input texts more readable without altering their meaning. |
| Approach: | They propose a taxonomy of errors that are used to analyze simplification models . they propose to use simplification methods to make input texts more readable . |
| Outcome: | The proposed models introduce errors that are not captured by existing evaluation metrics. |
Requirements and Motivations of Low-Resource Speech Synthesis for Language Revitalization (2022.acl-long)
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| Challenge: | Existing research on speech synthesis systems for three Indigenous languages in Canada requires tens of hours of audio recordings to be trained. |
| Approach: | They build a system for three Indigenous languages spoken in Canada using 1 hour of training data and 10 hours of data to train low-resource models. |
| Outcome: | The proposed system can produce speech with comparable naturalness to a Tacotron2 model trained with 10 hours of data. |
Sharpness-Aware Minimization Improves Language Model Generalization (2022.acl-long)
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| Challenge: | Comparatively little work has been done to improve the generalization of language models . recent work shows that Sharpness-Aware Minimization (SAM) can improve generalization without much computational overhead. |
| Approach: | They propose a Sharpness-Aware Minimization procedure that encourages convergence to flatter minima to improve generalization of language models without much computational overhead. |
| Outcome: | The proposed Sharpness-Aware Minimization procedure can improve language models without much computational overhead. |
Adversarial Authorship Attribution for Deobfuscation (2022.acl-long)
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| Challenge: | Existing authorship attribution approaches do not consider adversarial threat model . authors show adversarially trained authorship attributors can degrade effectiveness of existing obfuscators from 20-30% to 5-10% . |
| Approach: | They propose to use rule-based and learning-based text obfuscation approaches to counter authorship attribution. |
| Outcome: | The proposed approaches do not consider the adversarial threat model . authors show that adversarially trained attributors can degrade effectiveness of existing obfuscators from 20-30% to 5-10% . |
Weakly Supervised Word Segmentation for Computational Language Documentation (2022.acl-long)
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| Challenge: | a recent paper aims to improve the effectiveness of unsupervised language analysis techniques in low resource settings. |
| Approach: | They propose to use a weak supervision to improve linguistic segmentation in low resource languages . they propose to provide linguists with LTs that can be used to create interactive annotation tools . |
| Outcome: | The proposed models can be used to improve the quality of language segmentation in low resource languages. |
SciNLI: A Corpus for Natural Language Inference on Scientific Text (2022.acl-long)
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| Challenge: | Existing Natural Language Inference (NLI) datasets are not related to scientific text. |
| Approach: | They propose a large dataset for NLI that captures the formality in scientific text and contains 107,412 sentence pairs extracted from scholarly papers on NLP and computational linguistics. |
| Outcome: | The proposed model achieves a Macro F1 score of only 78.18% and an accuracy of 78.23%. |
Neural reality of argument structure constructions (2022.acl-long)
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| Challenge: | lexicalist linguistic theories assume argument structure is predictable from meaning of verbs . construction grammarians propose argument structure constructions distinct from verbs. |
| Approach: | They adapt psycholinguistic studies to probe for the existence of argument structure constructions in Transformer-based language models. |
| Outcome: | The proposed method could be used to probe argument structure constructions in LMs . the study shows that LM learners prefer grouping by construction over verb grouping . |
On the Robustness of Offensive Language Classifiers (2022.acl-long)
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| Challenge: | Existing studies on offensive language classifiers have focused on primitive attacks such as misspellings and extraneous spaces. |
| Approach: | They analyze the robustness of offensive language classifiers against crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
| Outcome: | The proposed classifiers are robust against more crafty attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (2022.acl-long)
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| Challenge: | Existing methods for few-shot style transfer often copy inputs verbatim . a new method is better at controlling the style transfer magnitude using an input scalar knob. |
| Approach: | They propose a method to model the stylistic difference between paraphrases by rewriting a sentence into a target style while preserving semantics. |
| Outcome: | The proposed method achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. |
ABC: Attention with Bounded-memory Control (2022.acl-long)
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Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, Noah A. Smith
| Challenge: | Existing approaches to attention with bounded-memory control (ABC) have a quadratic complexity in sequence lengths, making it prohibitive for long sequences. |
| Approach: | They propose a new abstraction that bounds memory size to improve efficiency . they propose bounded-memory control, which connects several efficient attention variants . |
| Outcome: | The proposed approach outperforms existing approaches on language modeling, machine translation, and masked language model finetuning. |
The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail (2022.acl-long)
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| Challenge: | Researchers in NLP often frame and discuss research results in ways that serve to deemphasize the field’s successes, often in response to the field's widespread hype. |
| Approach: | They propose to use more rigorous evaluation techniques to avoid false claims about the limits of our best technology. |
| Outcome: | This paper urges researchers to be careful about these claims and suggests research directions and communication strategies that will make it easier to avoid or rebut them. |
RELiC: Retrieving Evidence for Literary Claims (2022.acl-long)
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| Challenge: | Literature scholars often provide evidence for claims they make in the form of quotations from a work . a novel evidence retrieval task requires a deep understanding of complex literary phenomena . |
| Approach: | They use a large-scale dataset to formulate a task of literary evidence retrieval . they implement a RoBERTa-based dense passage retriever that outperforms existing methods . |
| Outcome: | The proposed model outperforms pretrained information retrieval baselines, but there is room for improvement. |
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas (2022.acl-long)
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| Challenge: | Recent work on visual-grounded navigation has focused on indoor scenarios with sharp drops in performance when testing on unseen data. |
| Approach: | They focus on visual agent navigation in outdoor scenarios with panorama images . they find that most gain in outdoor VLN on unseen data is due to specific features . |
| Outcome: | The results show a bias to specifics of graph representations of urban environments, demanding that VLN tasks grow in scale and diversity of geographical environments. |
Adapting Coreference Resolution Models through Active Learning (2022.acl-long)
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| Challenge: | Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. |
| Approach: | They investigate how to actively label coreference by sampling a small subset of data for annotators to label. |
| Outcome: | The proposed model can be more realistic when labeling spans within the same document than when annotating spans across documents. |
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models (2022.acl-long)
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| Challenge: | et al. (2017) show that imitation learning algorithms for machine translation introduce mismatches between training and inference that lead to undertraining and poor generalization in editing scenarios. |
| Approach: | They propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks where the original input sequence is iteratively edited to produce the output. |
| Outcome: | The proposed framework significantly improves output quality and controls complexity better on the simplification task. |
Memorisation versus Generalisation in Pre-trained Language Models (2022.acl-long)
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| Challenge: | State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. |
| Approach: | They propose to extend pre-trained language models to generalise and memorise facts in noisy and low-resource scenarios. |
| Outcome: | The proposed extension improves performance in low-resource named entity recognition tasks. |
ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments (2022.acl-long)
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| Challenge: | Existing automated evaluation systems of chatbots rely on static chat scripts as ground truth, which is hard to obtain. |
| Approach: | They propose an interactive chatbot evaluation framework that allows chatbots to compete with each other like in a sports tournament. |
| Outcome: | The proposed framework can rank chatbots independently from their model architectures and domains . existing evaluation systems rely on static chat scripts as ground truth . |
Do self-supervised speech models develop human-like perception biases? (2022.acl-long)
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| Challenge: | Recent advances in speech recognition and representation learning show that self-supervised pretraining is an excellent way of improving performance while reducing the amount of labelled data needed for training. |
| Approach: | They compare the representational spaces of wav2vec, HuBERT and contrastive predictive coding (CPC) with the perceptual spaces of French-speaking and English-speaking human listeners. |
| Outcome: | The proposed models capture fine-grained perceptual phenomena while supervised models are better at capturing coarser, phone-level effects and effects of listeners’ native language on perception. |
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions (2022.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence. |
| Approach: | They propose to build an embodied agent that can communicate with humans in natural language and navigate in real 3D environments. |
| Outcome: | This paper reviews current studies in the emerging field of vision-and-language navigation . it highlights limitations and opportunities for future work . |
Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing (2022.acl-long)
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| Challenge: | Existing methods for table fact verification do not study generating latent programs from statements . current weakly supervised methods are limited due to huge search space with lots of spurious programs. |
| Approach: | They propose a structure-aware approach to do table fact verification by structure-based parsing . they leverage lexical features and structure features to generate program from statements . |
| Outcome: | The proposed method generates programs more accurately than existing parsers and achieves comparable performance to the SOTA on the large-scale benchmark TABFACT. |
Cluster & Tune: Boost Cold Start Performance in Text Classification (2022.acl-long)
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| Challenge: | Existing methods to fine-tune pre-trained models for text classification are poor in practice. |
| Approach: | They propose to add an intermediate unsupervised classification task between pre-training and fine-tuning phases to boost performance of pre-trained models. |
| Outcome: | The proposed method improves performance on topical classification tasks when labeled data is scarce. |
Overcoming a Theoretical Limitation of Self-Attention (2022.acl-long)
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| Challenge: | Hahn shows that for languages where acceptance depends on a single input symbol, a transformer’s classification decisions get closer and closer to random guessing as input strings get longer and longer. |
| Approach: | They propose a transformer that recognizes PARITY with perfect accuracy and a model that uses layer normalization to bring the cross-entropy of both models arbitrarily close to zero. |
| Outcome: | The proposed model can accept and reject strings with perfect accuracy and bring cross-entropy close to zero when they need to focus on a single position. |
Prediction Difference Regularization against Perturbation for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing methods for regularizing input perturbation are limited by under-fitting of training data. |
| Approach: | They propose a method that can reduce over-fitting and under-fitting at the same time. |
| Outcome: | The proposed method can reduce over-fitting and under-fitturing while making the model less sensitive to small input changes and more robust to under-perturbed training data. |
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
| Approach: | They propose to use multilingual pre-trained models to model cross-lingual transfer in a selection of target languages. |
| Outcome: | The proposed model can be used to improve cross-lingual transfer performance in low-resource languages with no labeled training data. |
Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm Generation (2022.acl-long)
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| Challenge: | sarcasm generation research focused on creating more human-like interactions . previous research focused only on how to generate text that people perceive as sarkastic . |
| Approach: | They propose a theory-driven framework for generating sarcastic responses that allows us to control linguistic devices included during generation. |
| Outcome: | The proposed framework allows us to control the linguistic devices included during generation. |
How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? (2022.acl-long)
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| Challenge: | Existing models for data-to-text generation are based on pipelines and end-to end architectures. |
| Approach: | They use multidimensional quality metrics to evaluate models on end-to-end data-totext generation and compare their performance against pipeline models. |
| Outcome: | The proposed model improves in Omission and Inaccuracy Extrinsic errors but increases errors such as Addition. |
Probing for Labeled Dependency Trees (2022.acl-long)
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| Challenge: | Existing linear probes are limited to extracting undirected or unlabeled dependency parse trees, thereby capturing only a subset of the full task. |
| Approach: | They propose a linear probe which can extract labeled and directed dependency parse trees from embeddings while using fewer parameters and compute than prior methods. |
| Outcome: | The proposed method outperforms baselines and prior work in 13 languages and outperformed competitive baselines. |
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation (2022.acl-long)
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| Challenge: | Existing domain adaptation (DA) algorithms are not able to handle out-of-distribution examples due to the costly and labor-intensive data labeling process. |
| Approach: | They propose a controllable generation approach to deal with domain adaptation challenge by generating a domain-counterfactual textual example from an input text. |
| Outcome: | The proposed approach outperforms baselines and improves accuracy of state-of-the-art unsupervised DA algorithm. |
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (2022.acl-long)
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| Challenge: | Existing structured document understanding models only deal with document data of specific language(s) this is extremely limited for other languages, especially in the case of lacking pre-training structured document data. |
| Approach: | They propose a language-independent Layout Transformer (LiLT) for structured document understanding . they propose to pre-train structured documents in a single language and fine-tune them on other languages . |
| Outcome: | The proposed model achieves competitive or even superior performance on diverse downstream benchmarks on eight languages. |
Dependency-based Mixture Language Models (2022.acl-long)
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| Challenge: | Existing models to incorporate syntactic structures into neural language models have relied heavily on elaborate components for a specific language model, which makes them unwieldy in practice to fit into other models. |
| Approach: | They propose a dependency-based mixture language model that incorporates syntactic structures into neural language models by mixing previous dependency modeling probabilities with self-attention. |
| Outcome: | The proposed method can be easily and effectively applied to different neural language models while improving neural text generation on various tasks. |
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining? (2022.acl-long)
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| Challenge: | Existing methods for argument mining are limited by the scarcity of manually annotated data and the highly domain-dependent nature of argumentation. |
| Approach: | They propose a novel transfer learning strategy to fine tune pretrained Transformer-based Language Models on a selectively masked language modeling task and a new prompt-based strategy for inter-component relation prediction. |
| Outcome: | The proposed method outperforms existing models on both within- and out-of-domain datasets while leveraging on the discourse context. |
Entity-based Neural Local Coherence Modeling (2022.acl-long)
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| Challenge: | Recent neural coherence models encode the input document using large-scale pretrained language models. |
| Approach: | They propose an entity-based neural local coherence model which is linguistically more sound than previous models. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks. |
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks (2022.acl-long)
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| Challenge: | Existing methods to detect adversarial text inputs are limited in performance and are not detectable via spell checkers. |
| Approach: | They propose a model-agnostic detector of adversarial text examples that detects patterns in the logits of the target classifier when perturbing the input text. |
| Outcome: | The proposed detector improves the state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks. |
Local Languages, Third Spaces, and other High-Resource Scenarios (2022.acl-long)
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| Challenge: | In one view, languages exist on a resource continuum and the challenge is to scale existing solutions, bringing under-resourced languages into the high-resource world. |
| Approach: | They propose to scale existing solutions to bring under-resourced languages into the high-resource world by bringing standardised languages into high-level global information society. |
| Outcome: | The proposed language technology agendas address the diverse situations of the world's languages. |
That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory (2022.acl-long)
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| Challenge: | Grammar, vocabulary, and lexical semantic shifts take place over time, creating diachronic linguistic gap. |
| Approach: | a Chinese cross-era learning framework incorporates era-specific linguistic knowledge into a neural network. |
| Outcome: | a new learning framework for Chinese word segmentation improves on four corpora from different eras. |
Fair and Argumentative Language Modeling for Computational Argumentation (2022.acl-long)
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| Challenge: | Recent work on stereotypical biases in semantic spaces is still in its infancy . we present a novel resource for bias measurement specifically tailored to argumentation . |
| Approach: | They propose a resource for bias measurement specifically tailored to argumentation . they use argumentative fine-tuning and debiasing to assess intrinsic bias . |
| Outcome: | The proposed approach is more sustainable and parameter-efficient than full fine-tuning . it can remove bias in general and argumentative language models while improving model performance in downstream tasks. |
Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation (2022.acl-long)
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| Challenge: | Existing methods to perform simultaneous speech-to-text translation ignore contextual information and suffer from low translation quality. |
| Approach: | They propose an adaptive segmentation policy for simultaneous speech-to-text translation . it learns to segment the source streaming speech into meaningful units . |
| Outcome: | The proposed method achieves a good accuracy-latency trade-off over state-of-the-art methods on English-German and Chinese-English. |
Can Pre-trained Language Models Interpret Similes as Smart as Human? (2022.acl-long)
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| Challenge: | Simile interpretation is a crucial task in natural language processing. |
| Approach: | They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions. |
| Outcome: | The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans. |
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)
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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
| Challenge: | a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages. |
| Approach: | They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models. |
| Outcome: | The proposed benchmarks show that the current models perform worse than the human ceiling. |
Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization (2022.acl-long)
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| Challenge: | Text summarization aims to generate a short summary for an input text. |
| Approach: | They propose a non-autoregressive unsupervised summarization approach which performs edit-based search towards a heuristicically defined score and generates a summary as pseudo-groundtruth. |
| Outcome: | The proposed approach achieves state-of-the-art performance for unsupervised summarization, while improving inference efficiency. |
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural machine translation (NMT) tasks require large amounts of parallel data to augment training. |
| Approach: | They propose a data augmentation paradigm that augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning. |
| Outcome: | The proposed paradigm improves on the state-of-the-art in supervised neural machine translation tasks. |
Lexical Knowledge Internalization for Neural Dialog Generation (2022.acl-long)
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| Challenge: | Existing knowledge-grounded dialog models ignore the knowledge that resides in people's minds during a conversation. |
| Approach: | They propose to integrate lexical knowledge internally into the model's parameters instead of further conditioning them on external knowledge . they adopt contrastive learning approach and use a dictionary-based token-level lexicon retriever that requires only weak supervision. |
| Outcome: | The proposed model can relate J.K Rowling to Khalsa Aid with the knowledge retrieved from Wikipedia. |
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models (2022.acl-long)
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| Challenge: | Existing methods for SRL identify semantic dependencies that specify the semantic role of arguments in relation to predicates. |
| Approach: | They propose a mixture model-based end-to-end method to model syntactic-semantic dependency correlation in Semantic Role Labeling. |
| Outcome: | The proposed method improves performance in English, German, and Spanish . it achieves small but statistically significant improvement over baseline methods . |
Learning the Beauty in Songs: Neural Singing Voice Beautifier (2022.acl-long)
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| Challenge: | Existing techniques for pitch correction are limited to intonation but ignore the overall aesthetic quality. |
| Approach: | They propose a novel time-warping approach for pitch correction to synchronize the amateur recording with the template pitch curve. |
| Outcome: | The proposed model improves intonation and vocal tone while keeping content and vocal timbre. |
A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation (2022.acl-long)
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| Challenge: | Existing models for introducing explicit personas are expensive due to their expensive collection costs. |
| Approach: | They propose a data manipulation method which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. |
| Outcome: | The proposed method is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. |
LinkBERT: Pretraining Language Models with Document Links (2022.acl-long)
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| Challenge: | Existing language model pretraining methods do not capture dependencies or knowledge that span across documents. |
| Approach: | They propose a language model pretraining method that leverages links between documents . they use masked language modeling and document relation prediction to model LMs . |
| Outcome: | The proposed method outperforms existing methods on downstream tasks across two domains. |
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs (2022.acl-long)
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| Challenge: | Temporal knowledge graphs record entity relations and when they occur in time . previous work fails to address time-related challenges such as time-order issues . paper proposes time-sensitive question answering framework to address these problems . |
| Approach: | They propose a time-sensitive question answering framework that uses temporal KGs to answer natural language questions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on a new benchmark for question answering over temporal knowledge graphs. |
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition (2022.acl-long)
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| Challenge: | a new approach for self-supervised speech representation learning is proposed . a phoneme inventory learning model is based on a discrete representation of speech . |
| Approach: | They propose a neural discrete representation learning model for self-supervised phoneme inventory learning with raw speech and word labels. |
| Outcome: | The proposed model learns better phoneme-level representations and lowers error rates on TIMIT and Mboshi benchmarks than previous state-of-the-art models. |
Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions (2022.acl-long)
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| Challenge: | Existing language models can't predict the probability distribution over next words or masked words because of a single hidden state. |
| Approach: | They propose a multi-facet softmax to address the limitations of softmax . they propose LMs that can predict the next word distribution better as their sizes grow . |
| Outcome: | The proposed model achieves two-fold improvements in the perplexity of GPT-2 and BERT. |
Ditch the Gold Standard: Re-evaluating Conversational Question Answering (2022.acl-long)
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| Challenge: | Existing conversational question answering systems provide natural-language answers to users in information-seeking conversations. |
| Approach: | They conduct the first large-scale human evaluation of state-of-the-art conversational question answering systems . they propose a question rewriting mechanism based on predicted history which better correlates with human judgments . |
| Outcome: | The proposed question rewriting mechanism better correlates with human judgments. |
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2022.acl-long)
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| Challenge: | Large pretrained language models can generate text classification results that match fully supervised models. |
| Approach: | They propose to use a few sample training to determine which permutations are performant . they use generative language models to construct an artificial development set . |
| Outcome: | The proposed model outperforms fully-supervised models in eleven text classification tasks. |
Situated Dialogue Learning through Procedural Environment Generation (2022.acl-long)
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| Challenge: | a key hypothesis in the pursuit towards creating goal-driven natural language-based agents is interactivity and environment grounding is critical for effective language learning. |
| Approach: | They augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents. |
| Outcome: | The authors augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of increasing difficulty for training agents to achieve such goals. |
UniTE: Unified Translation Evaluation (2022.acl-long)
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| Challenge: | Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities . |
| Approach: | They propose a unified framework engaged with abilities to handle all three evaluation tasks. |
| Outcome: | The proposed framework can universally surpass state-of-the-art or winner methods across tasks. |
Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)
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| Challenge: | Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult. |
| Approach: | They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB. |
| Outcome: | The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP. |
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to build multi-way aligned corpus from bilingual data are limited by their scale. |
| Approach: | They propose to build a multi-way aligned corpus from bilingual data using two steps to extract candidate alignes and generate the final alignets from the candidates. |
| Outcome: | The proposed method improves on two publicly available datasets with +1.1 and +1.4 BLEU points. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)
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| Challenge: | Existing methods to detect sarcasm target with text lacking context are not sufficient and complete. |
| Approach: | They propose a multi-modal sarcasm target identification task that performs both textual and visual detection. |
| Outcome: | The proposed model can perform textual target labeling and visual target detection. |
Flexible Generation from Fragmentary Linguistic Input (2022.acl-long)
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| Challenge: | dominant paradigm for high-performance models in novel language tasks is direct specialization via training from scratch or fine-tuning large pre-trained models. |
| Approach: | They propose a new model that makes it possible to infer human behavior through basic computational motifs. |
| Outcome: | The proposed model outperforms direct-specialization models in three evaluations and performs comparable to human models. |
Revisiting Over-Smoothness in Text to Speech (2022.acl-long)
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| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
Coherence boosting: When your pretrained language model is not paying enough attention (2022.acl-long)
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| Challenge: | Long-range semantic coherence remains a challenge in automatic language generation and understanding. |
| Approach: | They propose a procedure that increases a model’s focus on a long context by distributional analyses of generated ordinary text and dialog responses. |
| Outcome: | The proposed procedure increases the model's focus on a long context. |
Uncertainty Estimation of Transformer Predictions for Misclassification Detection (2022.acl-long)
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Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov
| Challenge: | Uncertainty estimation (UE) of model predictions is crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, etc. |
| Approach: | They propose to modify UE methods for Transformer models for misclassification detection in named entity recognition and text classification tasks to improve model expressiveness and computational performance. |
| Outcome: | The proposed methods outperform computationally intensive methods on misclassification detection tasks and are based on a large dataset of simulated datasets. |
VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena (2022.acl-long)
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| Challenge: | Current models have difficulty addressing most phenomena, despite performance gains on many V&L tasks. |
| Approach: | They propose a benchmark for testing general-purpose pretrained vision and language models for their visio-linguistic grounding capabilities on specific linguistic phenomena. |
| Outcome: | The proposed model can be used to evaluate models on linguistic phenomena in the visual modality. |
The Grammar-Learning Trajectories of Neural Language Models (2022.acl-long)
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| Challenge: | In this paper, we show that neural language models with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
| Approach: | They propose to use mutual inductive bias to study linguistic representations implicit in NLMs. |
| Outcome: | The proposed approach shows that NLMs with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
Generating Scientific Definitions with Controllable Complexity (2022.acl-long)
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| Challenge: | Unfamiliar terminology and complex language can make understanding science difficult for readers. |
| Approach: | They propose a task and dataset for defining scientific terms and controlling the complexity of generated definitions by a sequence-to-sequence approach. |
| Outcome: | The proposed system is based on a sequence-to-sequence approach and human evaluations show it offers superior fluency while controlling complexity. |
Label Semantic Aware Pre-training for Few-shot Text Classification (2022.acl-long)
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| Challenge: | Existing models for text classification use label semantics but few studies have attempted to give models access to informative representations of labels. |
| Approach: | They propose to use label semantics to train generative models by performing secondary pre-training on labeled sentences from a variety of domains. |
| Outcome: | The proposed approach improves generalization and data efficiency of text classification systems while maintaining comparable performance to state-of-the-art models. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
A Comparison of Strategies for Source-Free Domain Adaptation (2022.acl-long)
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| Challenge: | Existing research on domain adaptation without access to training data is limited due to privacy concerns. |
| Approach: | They compare active learning, self-training, and data augmentation strategies for source-free domain adaptation with a shared task. |
| Outcome: | The proposed algorithms yield consistent gains across all SemEval 2021 Task 10 tasks and domains, but they are unreliable for source-free domain adaptation. |
Ethics Sheets for AI Tasks (2022.acl-long)
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| Challenge: | a recent study has shown that technology can lead to more adverse outcomes for marginalized populations . a new effort is called Ethics Sheets for AI Tasks to flesh out ethical considerations . |
| Approach: | a new effort will focus on ethical considerations at the level of AI tasks . authors propose a template for ethics sheets with 50 ethical consideration examples . |
| Outcome: | a new form of ethics sheets for AI tasks aims to flesh out assumptions and ethical considerations hidden in how a task is commonly framed . a template for ethics sheets with 50 ethical consideration, using the task of emotion recognition as an example, will be presented . |
Learning Disentangled Representations of Negation and Uncertainty (2022.acl-long)
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| Challenge: | Negation and uncertainty modeling are long-standing tasks in natural language processing. |
| Approach: | They propose to disentangle negation, uncertainty, and content using a Variational Autoencoder by supervising latent representations using auxiliary objectives. |
| Outcome: | The proposed model can disentangle negation, uncertainty, and content using a Variational Autoencoder. |
latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)
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| Challenge: | Recent advances in text generation have limited applications due to multimodality problem. |
| Approach: | They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem. |
| Outcome: | The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm. |
PPT: Pre-trained Prompt Tuning for Few-shot Learning (2022.acl-long)
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| Challenge: | Prompt tuning for pre-trained language models has shown remarkable performance . however, prompt tuning is still not fully explored . |
| Approach: | They propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. |
| Outcome: | The proposed framework outperforms full-model tuning under full-data and few-shot learning settings. |
Deduplicating Training Data Makes Language Models Better (2022.acl-long)
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Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini
| Challenge: | Existing language modeling datasets contain near-duplicate examples and long repetitive substrings. |
| Approach: | They develop tools that allow us to deduplicate existing language modeling datasets . they found that over 1% of the unprompted output of language models is copied verbatim . |
| Outcome: | The proposed tools reduce train-test overlap, which affects over 4% of validation sets, and improve model accuracy. |
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires (2022.acl-long)
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| Challenge: | Existing approaches to identify mental health conditions using social media are limited by the presence of symptoms described in a questionnaire used by clinicians. |
| Approach: | They propose to ground a model in PHQ9's symptoms to improve generalization . they also show that this approach can still perform competitively on in-domain data. |
| Outcome: | The proposed approach can perform competitively on in-domain data while improving generalizability and generalisability. |
Internet-Augmented Dialogue Generation (2022.acl-long)
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| Challenge: | Large language models are known to hallucinate facts when generating dialogue, and are unable to encode the knowledge in the model at the point of training. |
| Approach: | They propose an approach that generates an internet search query based on the context and conditions on the results to generate a response. |
| Outcome: | The proposed model generates an internet search query and then conditions on the results to generate a response. |
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022.acl-long)
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Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy Liu, Cheng-I Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee
| Challenge: | Existing evaluation methods for transfer learning are limited in speech research . authors show that pre-trained models transfer well across multiple tasks . |
| Approach: | They propose a benchmark to evaluate pre-trained models by increasing task diversity and difficulty over SUPERB. |
| Outcome: | The proposed benchmark increases task diversity and difficulty over SUPERB-SG. |
Knowledge Neurons in Pretrained Transformers (2022.acl-long)
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| Challenge: | Existing studies show that pretrained language models are good at recalling factual knowledge without fine-tuning. |
| Approach: | They propose a method to identify neurons that express factual knowledge in pretrained Transformers by filling-in-the-blank cloze queries. |
| Outcome: | The proposed method can be used to edit, erase, and update factual knowledge without fine-tuning. |
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (2022.acl-long)
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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
| Challenge: | Meta-learning can help overcome resource scarcity in cross-lingual NLP problems . pre-training of models requires large annotated training sets for the task at hand . |
| Approach: | They propose to use meta-learning to train a model to learn a parameter initialization that can adapt quickly to new languages. |
| Outcome: | The proposed model-agnostic meta-learning improves on language transfer and standard supervised learning baselines for unseen, typologically diverse, and low-resource languages in a few-shot learning setup. |
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English (2022.acl-long)
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| Challenge: | We introduce 1,679 sentence pairs in French that cover stereotypes in ten types of bias like gender and age. |
| Approach: | They build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages and cultures. |
| Outcome: | The proposed dataset allows for comparability across languages while characterizing biases that are specific to each country and language. |
Few-Shot Learning with Siamese Networks and Label Tuning (2022.acl-long)
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| Challenge: | Recent studies have shown that few-shot text classification is a poor solution for training data-intensive tasks. |
| Approach: | They propose a method that embeds texts and labels into classifiers with proper pre-training. |
| Outcome: | The proposed approach reduces inference cost by increasing the number of labels and embeddings. |
Inferring Rewards from Language in Context (2022.acl-long)
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| Challenge: | a new study grounding language to reward functions extends the standard instruction following setup in this way. |
| Approach: | They propose a model that infers rewards from language pragmatically by reasoning about how speakers choose utterances to elicit desired actions and reveal information about their preferences. |
| Outcome: | The proposed model infers rewards from language pragmatically on a flight–booking task with natural language. |
Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies (2022.acl-long)
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| Challenge: | Existing efforts to encourage article creation focus on reducing the gender gap in Wikipedia articles. |
| Approach: | They propose a model that retrieves web evidence and generates biographies section by section . they analyze available web evidence to determine the accuracy of the generated text . |
| Outcome: | The proposed model can generate biographies section by section, including citation information, using retrieval mechanisms and a cache-based pre-trained encoder-decoder. |
Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (2022.acl-long)
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| Challenge: | Automated short answer grading systems lack content-focused elaborated feedback datasets. |
| Approach: | They propose to use the Short Answer Feedback dataset to encourage research on explainable and understandable feedback systems. |
| Outcome: | The proposed model can be used to grade answers and explain where and why mistakes were made. |
Towards Better Characterization of Paraphrases (2022.acl-long)
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| Challenge: | Existing models of natural language processing lack generalization and performance . existing models are often overreliant on learned spurious correlations resulting in poor generalization. |
| Approach: | They propose to use word position deviation and lexical deviation to characterize paraphrase pairs without expert human annotation. |
| Outcome: | The proposed metrics improve generalizability of models trained on the dataset and can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models. |
SummScreen: A Dataset for Abstractive Screenplay Summarization (2022.acl-long)
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| Challenge: | Existing summarization datasets are constructed from various domains, such as news, and we characterize them using two entity-centric metrics. |
| Approach: | They propose to use a summarization dataset to evaluate TV series transcripts and recaps . they propose to employ two entity-centric metrics to evaluate the dataset . |
| Outcome: | The proposed model outperforms the existing model and its oracle counterparts in character overlap and accuracy. |
Sparsifying Transformer Models with Trainable Representation Pooling (2022.acl-long)
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| Challenge: | Existing approaches to sparsify attention in the Transformer model are based on quadratic memory complexity and a lack of information for each word. |
| Approach: | They propose a method to sparsify attention in a Transformer model by learning to select the most-informative token representations during the training process. |
| Outcome: | The proposed model performs better than the current SOTA model while being 1.8 faster during training, 4.5 faster inference and 13 more efficient in the decoder. |
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models (2022.acl-long)
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| Challenge: | In many natural language processing tasks the same input can have multiple possible outputs. |
| Approach: | They propose a novel exact n-best search algorithm for neural sequence models to measure sentence-level uncertainty by computing the degree of overlap between references from two different NLP tasks. |
| Outcome: | The proposed algorithm overly spreads the probability mass for uncertain tasks and sentences. |
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)
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| Challenge: | Existing methods for text data augmentation are limited to simple tasks and weak baselines. |
| Approach: | They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data. |
| Outcome: | The proposed method improves many tasks while not negatively affecting the others. |
Text-Free Prosody-Aware Generative Spoken Language Modeling (2022.acl-long)
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Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
| Challenge: | Experimental results show that generative spoken language models (LMs) are natural unsupervised multitask learners. |
| Approach: | They propose a prosody-aware generative spoken language model that uses discovered units to generate natural, meaningful, and coherent speech. |
| Outcome: | The proposed model can generate natural, meaningful, and coherent speech given a spoken prompt. |
Lite Unified Modeling for Discriminative Reading Comprehension (2022.acl-long)
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| Challenge: | generative and discriminative MRCs focus on answer generation, extractive MRC on answer extraction. |
| Approach: | They propose a lightweight POS-Enhanced Iterative Co-Attention Network to handle diverse discriminative MRC tasks synchronously. |
| Outcome: | The proposed model improves on four discriminative MRC benchmarks. |
Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining (2022.acl-long)
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| Challenge: | a model trained to align only two languages can encode multilingually more aligned representations . a dual-pivot transfer theory is proposed for bilingual training . |
| Approach: | They propose methods for learning cross-lingual sentence representations using paired or unpaired bilingual texts. |
| Outcome: | The proposed models reach the state of the art in unsupervised bitext mining and perform better than multilingually supervised models. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization (2022.acl-long)
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| Challenge: | a release note is a technical document that describes the latest changes to a software product. |
| Approach: | They propose to extract and then abstract release notes from GitHub repositories using a transformer-based network like BART. |
| Outcome: | The proposed methods generate less noisy release notes at higher coverage than baselines. |
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge (2022.acl-long)
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| Challenge: | Existing methods to improve machine reading comprehension (MRC) tasks require unstated knowledge to perform well. |
| Approach: | They propose to extract a new kind of structured knowledge from scripts and use it to improve machine reading comprehension (MRC) They propose a teacher-student paradigm to facilitate the transfer of knowledge in weakly-labeled MRC data. |
| Outcome: | The proposed method outperforms methods that use weakly-labeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C3. |
Modeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing (2022.acl-long)
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| Challenge: | Argumentation is an omnipresent rudiment of daily communication and thinking . humans struggle to develop argumentation skills due to a lack of individual and instant feedback in their learning process. |
| Approach: | They propose an argumentation annotation approach to model argumentative discourse in student-written business model pitches and embed it into an adaptive writing support system for students that provides individual argumentation feedback. |
| Outcome: | The proposed method annotates a corpus of 200 business model pitches in german and measures their self-efficacy and ease-of-use in a real-world writing exercise. |
Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons (2022.acl-long)
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| Challenge: | Recent studies show that evaluating NLG systems using pairwise comparisons is expensive as the number of human annotations grows linearly with k. |
| Approach: | They propose a framework to efficiently identify the top-ranked system by actively choosing system pairs for comparison using dueling bandit algorithms. |
| Outcome: | The proposed framework reduces human annotations by 80% on 13 NLG evaluation datasets spanning 5 tasks . |
The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments (2022.acl-long)
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| Challenge: | Existing arguments that focus on shared values are based on prior beliefs and morals, but little research has been done on the effectiveness of these proxies. |
| Approach: | They propose a system that automatically generates arguments focusing on different morals and ask liberals and conservatives to evaluate the impact of these arguments. |
| Outcome: | The proposed system generates arguments focusing on different morals, and the results are compared with existing arguments. |
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection (2022.acl-long)
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| Challenge: | Existing models that use heuristics to shorten sequence lengths are computationally prohibitive. |
| Approach: | They propose a new method to shorten sequence lengths by transforming tokens through encoders and a core-set based token selection method that avoids expensive pre-training and fine tuning. |
| Outcome: | The proposed model outperforms existing models on GLUE benchmarks and Long Range Arena datasets and demonstrates that it is cost-effective and space-efficient. |
Probing for the Usage of Grammatical Number (2022.acl-long)
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| Challenge: | Pre-trained language models can be used to perform a wide array of NLP tasks, but their encoding is still a mystery. |
| Approach: | They propose a usage-based probing setup to find an encoding that the model actually uses, and propose 'a use-based approach' they propose to use a behavioral task to remove the linguistic property, and to identify which encodes are used to transfer information from a noun to its head verb. |
| Outcome: | The proposed encodings are based on a behavioral task which cannot be solved without the linguistic property. |