Findings of the Association for Computational Linguistics: NAACL 2022
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking (2022.findings-naacl)
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| Challenge: | Fact-checking is the task of establishing the veracity of factual information, commonly performed manually by journalists. |
| Approach: | They propose a table fact-checking dataset based on real world public health claims and noisy evidence tables from sources similar to those used by fact checkers. |
| Outcome: | The proposed dataset achieves an overall F1 score of 0.73 . |
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context (2022.findings-naacl)
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| Challenge: | Current benchmarks do not evaluate numeracy of pretraining language models on measurements. |
| Approach: | They propose a new task where a model learns to reconstruct a number with its associated unit given masked text. |
| Outcome: | The proposed model significantly underperforms pre-trained model with baselines and ablations. |
PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)
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| Challenge: | Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. |
| Approach: | They propose a model PromptGen which can automatically generate prompts conditional on the input sentence. |
| Outcome: | The proposed model outperforms baseline models on the knowledge probing LAMA benchmark. |
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
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| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
SEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models (2022.findings-naacl)
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| Challenge: | Recent research shows promising results on combining pretrained language models with canonical utterance for few-shot semantic parsing. |
| Approach: | They propose a few-shot semantic parsing method that decomposes a problem into a sequence of sub-problems, which correspond to the sub-clauses of the formal language. |
| Outcome: | The proposed method achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split. |
MultiVerS: Improving scientific claim verification with weak supervision and full-document context (2022.findings-naacl)
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| Challenge: | a new approach to scientific claim verification uses a document-level fact-checking label to label scientific documents . a multitask approach combines a shared encoding of the claim and document context . |
| Approach: | They propose a system which predicts a fact-checking label and identifies rationales in a multitask fashion based on a shared encoding of the claim and full document context. |
| Outcome: | The proposed approach outperforms baselines on three scientific claim verification datasets . it can learn from instances annotated with a document-level fact-checking label, but lacking sentence-level rationales based on the datasets. |
An Item Response Theory Framework for Persuasion (2022.findings-naacl)
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| Challenge: | Several studies have considered the audience as a whole or by building separate models for different types of audiences. |
| Approach: | They propose a framework that can represent individual audience members in one model across a diverse set of persuasion tasks. |
| Outcome: | The proposed model performs well on three datasets including a novel dataset in the area of political advocacy. |
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks (2022.findings-naacl)
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| Challenge: | Existing methods to improve model robustness against word substitution-based adversarial attacks are too slow to generate adversarials on the fly. |
| Approach: | They propose an approach to improve the robustness of BERT models against word substitution-based adversarial attacks by leveraging adversarials for self-supervised contrastive learning. |
| Outcome: | The proposed method improves robustness of BERT models against word substitution-based adversarial attacks without using any labeled data. |
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation (2022.findings-naacl)
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| Challenge: | Question generation models are often evaluated with standardized NLG metrics that are based on n-gram overlap. |
| Approach: | They propose to use QGen to help teachers automate the generation of reading comprehension quizzes by comparing n-gram overlap with BLEU to compare system-generated questions with heldout human-written references. |
| Outcome: | The best model had only 68.4% of its questions accepted by the ten teachers who participated in the study. |
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning (2022.findings-naacl)
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| Challenge: | Experimental results show that the proposed model outperforms single-task baseline by 3% and multi-task (without instruction) baseline by 18% on an average. |
| Approach: | They propose a unified model that can learn all 32 instruction tasks of the BoX without any task-specific modules. |
| Outcome: | The proposed model outperforms single-task baseline by 3% and multi-task (without instruction) baseline by 18% on an average. |
How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train & Few-shot Cross-lingual Transfer (2022.findings-naacl)
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| Challenge: | Recent studies have focused on zero-shot cross-lingual transfer of pretrained languages. |
| Approach: | They propose to use few-shot cross-lingual transfer to improve zero-shot performance of multilingual pretrained language models. |
| Outcome: | The proposed model can be scaled to high-quality samples and improves on zero-shot performance. |
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge (2022.findings-naacl)
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| Challenge: | Existing open-domain question answering systems only select one source to generate answer or conduct reasoning on structured information. |
| Approach: | They propose a Document-Entity Heterogeneous Graph Network to integrate different sources of information and conduct reasoning on heterogeneous information. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on a HybirdQA dataset. |
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks (2022.findings-naacl)
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Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang, Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
| Challenge: | Increasing concerns and regulations about data privacy necessitate the study of privacy-preserving, decentralized learning methods for natural language processing tasks. |
| Approach: | They propose a framework for evaluating federated learning methods on four different tasks . they propose federation between Transformer-based language models and FL methods . |
| Outcome: | The proposed framework compares FL methods on four different tasks under non-IID partitioning strategies. |
SemAttack: Natural Textual Attacks via Different Semantic Spaces (2022.findings-naacl)
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| Challenge: | Existing approaches to attack pre-trained language models suffer from low success rates or fail to search efficiently in the exponentially large perturbation space. |
| Approach: | They propose an efficient framework to generate natural adversarial text by constructing different semantic perturbation functions. |
| Outcome: | The proposed framework generates natural adversarial texts for different languages with high success rates. |
Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription (2022.findings-naacl)
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| Challenge: | Document transcription models are limited by extremely varied style and content across domains. |
| Approach: | They propose a self-supervised approach for learning rich visual representations for both handwritten and printed historical document transcription using a heterogeneous set of handwritten Islamicate manuscript images and early modern English printed documents. |
| Outcome: | The proposed model improves on a supervised model with as few as 30 line image transcriptions on two languages with a single line of image training. |
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models (2022.findings-naacl)
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| Challenge: | Existing work on cross-lingual transfer has not studied how to leverage knowledge of rich-resource languages without labels. |
| Approach: | They propose a 2-step knowledge distillation framework to achieve knowledge transfer from off-the-shelf models in rich-resource languages. |
| Outcome: | The proposed method reduces annotation cost and protects private labels. |
Opportunities for Human-centered Evaluation of Machine Translation Systems (2022.findings-naacl)
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| Challenge: | a new study examines the role of machine translation in larger user-facing systems . a sysadmin and a human factors researcher are developing evaluation tools . |
| Approach: | They argue that machine translation models are embedded in larger user-facing systems . they argue that evaluation at the systems level is still lacking . |
| Outcome: | The proposed model evaluations are based on human-computer interaction models . the authors argue that evaluations should be based more on the entire system . |
Aligning Generative Language Models with Human Values (2022.findings-naacl)
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| Challenge: | Existing methods for learning human values do not consider contextual and abstract nature of human values. |
| Approach: | They propose a reinforcement learning based method that embeds human values judgements into each step of language generation. |
| Outcome: | The proposed method improves on human values judgements and shows higher alignment performance. |
PerKGQA: Question Answering over Personalized Knowledge Graphs (2022.findings-naacl)
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| Challenge: | Existing methods for question answering over knowledge graphs have focused on generalizable or generic knowledge, which assumes there is a predefined global KG for all queries. |
| Approach: | They propose to use a non-parametric technique that employs case-based reasoning and a parametric approach using graph neural networks to query a predefined knowledge graph (KG) |
| Outcome: | The proposed methods outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%. |
Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)
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| Challenge: | Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. |
| Approach: | They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives. |
| Outcome: | The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora . |
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection (2022.findings-naacl)
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| Challenge: | Existing approaches to detect novel intents have been tested in the last decade. |
| Approach: | They propose a framework to detect multiple novel intents with budgeted human annotation cost. |
| Outcome: | The proposed framework outperforms baseline methods in terms of accuracy and F1-score on a set of benchmark datasets. |
Design Challenges for a Multi-Perspective Search Engine (2022.findings-naacl)
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| Challenge: | a document retrieval system fails to deliver diverse and direct responses to controversial questions . classical document retrievals provide a ranked list of references to relevant but not necessarily trustworthy web documents . |
| Approach: | They propose a perspective-oriented document retrieval paradigm to address these challenges . they propose sponses with different perspectives within topically-related web documents . |
| Outcome: | The proposed system is based on a user survey and a prototype . it will be used to assess the utility and understanding of the system . |
Exploring the Value of Multi-View Learning for Session-Aware Query Representation (2022.findings-naacl)
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| Challenge: | Existing approaches to learn distributed query representations only consider user’s query reformulations or system’s rankings . previous studies show that user’ s query behavior and knowledge change depending on the system’ 'results' and intertwine and affect each other during the completion of a search task. |
| Approach: | They propose to use multi-view learning methods to align query embeddings with document ranking representations using transformers. |
| Outcome: | The proposed approach can capture search intent semantics and can reflect user's query behavior and knowledge. |
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)
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| Challenge: | Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems. |
| Approach: | They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling. |
| Outcome: | The proposed model achieves state-of-the-art on benchmarks and in overall and long-tail performance. |
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting (2022.findings-naacl)
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| Challenge: | Pre-trained language models (PLMs) are the state-of-the-art (SOTA) models for natural language processing (NLP). |
| Approach: | They propose a patient and confident early exiting BERT (PCEE-BERT) that can work with different PLMs and popular model compression methods. |
| Outcome: | The proposed method outperforms existing models on the GLUE benchmarks and achieves different speed-up ratios. |
Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback (2022.findings-naacl)
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| Challenge: | Our approach pairs an LM with a growing memory of cases where the user identified an output error and provided general feedback on how to correct it. |
| Approach: | They propose to use an existing script generator to train a model to repair output errors without retraining. |
| Outcome: | The proposed model learns to apply user feedback to repair output errors while avoiding similar past mistakes on new, unseen examples. |
One Size Does Not Fit All: The Case for Personalised Word Complexity Models (2022.findings-naacl)
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| Challenge: | Complex word identification (CWI) aims to identify words in a text that are difficult for a reader to understand and therefore benefit from simplification. |
| Approach: | They propose to use a novel active learning framework to tailor models to individual readers and release a dataset of complexity annotations and models as a benchmark for further research. |
| Outcome: | The proposed model can be tailored to individual readers and released as a benchmark for future research. |
TEAM: A multitask learning based Taxonomy Expansion approach for Attach and Merge (2022.findings-naacl)
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| Challenge: | Existing methods for automating taxonomy expansion are attach and merge . elucidating the problem of limited coverage of WordNets is presented . |
| Approach: | They propose a multitask learning-based deep learning method that performs both merge and attach operations in a single model. |
| Outcome: | The proposed method outperforms state-of-the-art models on three WordNet taxonomies . it performs both merge and attach operations and also provides encouraging performance for merge operation . |
Extracting Temporal Event Relation with Syntax-guided Graph Transformer (2022.findings-naacl)
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| Challenge: | Temporal relationship extraction is crucial for understanding complex events and reasoning over them. |
| Approach: | They propose a Syntax-guided Graph Transformer network to extract temporal relations between events by explicitly exploiting the connection between two events based on their dependency parsing trees. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on MATRES and TB-DENSE with up to 7.9% absolute F-score gain. |
From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory (2022.findings-naacl)
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| Challenge: | Fuzzy trace theory explains human risky decision-making by incorporating gists, i.e. fuzzy representations of information which capture only its quintessential meaning. |
| Approach: | They propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. |
| Outcome: | The proposed framework can be optimised to predict risky decision-making in groups and individuals. |
Few-Shot Self-Rationalization with Natural Language Prompts (2022.findings-naacl)
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| Challenge: | Existing models that generate free-text explanations for tasks are limited by human-written explanations. |
| Approach: | They propose to use a standardized collection of natural language prompts to create a model that generates free-text explanations for tasks. |
| Outcome: | The proposed model can predict task labels and generate free-text explanations for predictions . plausibility of human explanations is 76%, while human explanation is 51% . |
DOCmT5: Document-Level Pretraining of Multilingual Language Models (2022.findings-naacl)
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| Challenge: | DOCmT5 is a multilingual sequence-to-sequence language model pretraining with large-scale parallel documents. |
| Approach: | They propose a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. |
| Outcome: | The proposed model improves on baselines on document-level generation tasks. |
Literature-Augmented Clinical Outcome Prediction (2022.findings-naacl)
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| Challenge: | Existing approaches to clinical outcome prediction use only clinical notes and general biomedical literature. |
| Approach: | They propose to retrieve patient-specific medical literature and incorporate it into predictive models by combining clinical notes with language models. |
| Outcome: | The proposed approach boosts predictive performance on three important clinical tasks in comparison to strong LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%. |
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text (2022.findings-naacl)
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| Challenge: | Existing approaches to few-shot relation classification have limited labeled examples . a prototype encoder from definition and an instance is needed to learn relation instance classification . |
| Approach: | They propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. |
| Outcome: | The proposed encoder outperforms state-of-the-art methods on several datasets. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
Multimodal Intent Discovery from Livestream Videos (2022.findings-naacl)
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Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
| Challenge: | Existing models for instructional video understanding struggle to understand abstract intents . identifying procedural intent within instructional videos is a challenging task . |
| Approach: | They propose to extract instructional intent from software instructional livestreams by using a multimodal cascaded cross-attention model that integrates weaker and noisier video signals with more discriminative text signals. |
| Outcome: | The proposed model improves on baseline models and compares it to existing models. |
A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations (2022.findings-naacl)
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Md Mosharaf Hossain, Luke Holman, Anusha Kakileti, Tiffany Kao, Nathan Brito, Aaron Mathews, Eduardo Blanco
| Challenge: | Negations carry affirmative meanings, which are difficult to process and understand by humans. |
| Approach: | They propose a question-answer driven approach to reveal affirmative interpretations from verbal negations. |
| Outcome: | The proposed approach is based on a natural language inference task . it shows that state-of-the-art transformers are insufficient to reveal affirmative interpretations . |
Harmless Transfer Learning for Item Embeddings (2022.findings-naacl)
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| Challenge: | Existing approaches to learn item embeddings for categorical features are limited by the frequency of items in real-world. |
| Approach: | They propose a method that transfers knowledge from frequent items to rare items by introducing an auxiliary transfer loss. |
| Outcome: | The proposed framework significantly boosts the performance on a variety of NLP and recommendation system tasks. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
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| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control (2022.findings-naacl)
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| Challenge: | Abstractive summarization systems have been shown to be more prone to unfaithful facts . 30% of summaries generated by pre-trained language models suffer from hallucination . |
| Approach: | They propose a method to remedy entity-level extrinsic hallucinations with Entity Coverage Control . they first compute entity coverage precision and prepend the corresponding control code . a further fine-tuning is performed to unlock zero-shot summarization . |
| Outcome: | The proposed method leads to more faithful and salient abstractive summarization in fine-tuning and zero-shot settings. |
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (2022.findings-naacl)
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| Challenge: | Existing methods to detect ideological divides in social media rely on knowing in advance the political orientation of text . fascist and mainstream are among the most polarized concepts in reddit in 2019 . |
| Approach: | They propose a minimally supervised method that leverages the network structure of online discussion forums to detect polarized concepts. |
| Outcome: | The proposed framework captures temporal ideological dynamics such as right-wing and left-wing radicalization using graph neural networks and sparsity learning. |
On Measuring Social Biases in Prompt-Based Multi-Task Learning (2022.findings-naacl)
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| Challenge: | a large body of work within prompt engineering attempts to understand the effects of input forms and prompts in achieving superior performance. |
| Approach: | They propose a large-scale text-to-text language model trained using prompts . they consider two different forms of semantically equivalent inputs - question-answer format and premise-hypothesis format . |
| Outcome: | The proposed model can generalize into novel forms of language and handle novel tasks. |
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System (2022.findings-naacl)
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| Challenge: | Recent research has focused on reinforcement learning (RL)-based dialogue policy. |
| Approach: | They propose a dynamic partial average estimator (DPAV) of the ground truth maximum action value to solve the overestimation problem. |
| Outcome: | The proposed method achieves better results on three dialogue datasets with a lower computational load compared to baselines on three different domains with lower bias. |
Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis (2022.findings-naacl)
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| Challenge: | PPCEME has a large set of function tags and is difficult to parse . authors present results for PPceME using a modified version of the Berkeley Neural Parser . |
| Approach: | They propose to use a modified version of the Berkeley Neural Parser to parse PPCEME using function tags. |
| Outcome: | The proposed parser will be used to parse Early English Books Online, a 1.5 billion word corpus. |
Instilling Type Knowledge in Language Models via Multi-Task QA (2022.findings-naacl)
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| Challenge: | Current methods to learn entity types rely on coarse, noisy labels . current methods rely only on text-to-text pre-training on type-centric questions . |
| Approach: | They propose to instill fine-grained type knowledge in language models by pre-training on type-centric questions. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot dialog state tracking benchmarks and can accurately infer entity types in Wikipedia articles. |
StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
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Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| Challenge: | Knowledge graphs (KGs) represent incomplete knowledge bases. |
| Approach: | They propose to use language models to extract semantic information from text descriptions while using Message Passing Neural Networks to capture structural information. |
| Outcome: | The proposed model achieves state of the art on three challenging inductive baselines. |
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality (2022.findings-naacl)
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| Challenge: | Specific problems arise when translating from English into languages with formality markers, such as “Are you sure?” . Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. |
| Approach: | They propose to train formality-controlled models by fine-tuning on labeled contrastive data and a metric to evaluate them. |
| Outcome: | The proposed model achieves high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality. |
CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations (2022.findings-naacl)
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| Challenge: | Using multilingual instructions to learn a better cross-lingual representation is challenging for multilingual agents. |
| Approach: | They propose to use multilingual instructions to learn a shared cross-lingual language representation for the three languages in a Room-Across-Room dataset. |
| Outcome: | The proposed model improves on the room-Across-room and vision-and-dialogue navigation tasks by maximizing similarity between semantically aligned image pairs from different environments. |
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting (2022.findings-naacl)
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Jesin James, Vithya Yogarajan, Isabella Shields, Catherine Watson, Peter Keegan, Keoni Mahelona, Peter-Lucas Jones
| Challenge: | Te reo Mori is New Zealand’s only indigenous language spoken by 4.5% of the population of 5 million. |
| Approach: | They train bilingual sub-word embeddings to detect Mori-English code-switching points using a cloud-based multilingual system such as Google and Microsoft Azure. |
| Outcome: | The proposed model outperforms large-scale contextual models on down streaming tasks of detecting Mori language. |
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation (2022.findings-naacl)
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| Challenge: | In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. |
| Approach: | They propose a ranker for inferring the priority order of the opponent from partial dialogues without needing additional annotations for training. |
| Outcome: | The proposed model performs better than baselines while accessing fewer utterances from the opponent. |
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework (2022.findings-naacl)
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| Challenge: | Recent work shows that large-scale pretrained language models (PLMs) are effective few-shot learners. |
| Approach: | They propose a method that treats few-shotlearners as crowdsourcing workers . they propose to use these workers to train models that solve a task well . |
| Outcome: | The proposed approach treats few-shotlearners as crowdsourcing workers . the resulting annotations can be utilized to train models that solve the task well . |
Entity Cloze By Date: What LMs Know About Unseen Entities (2022.findings-naacl)
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| Challenge: | Existing literature provides benchmarks to measure LMs' knowledge about entities . |
| Approach: | They propose a framework to analyze what language models can infer about new entities that did not exist when they were pretrained. |
| Outcome: | The proposed framework shows that models more informed about the entities achieve lower perplexity on this benchmark. |
Data Augmentation for Low-Resource Dialogue Summarization (2022.findings-naacl)
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| Challenge: | DADS generates synthetic examples by replacing sections of text from input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the simulated dialogue. |
| Approach: | They propose a Data Augmentation technique for low-resource Dialogue Summarization that uses pretrained language models to generate diverse alternatives. |
| Outcome: | The proposed method generates synthetic examples from a low-resource dataset . it produces topically diverse examples without introducing additional hallucinations . |
A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning (2022.findings-naacl)
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| Challenge: | Existing training paradigms for dialogue policy learning with brute-force random sampling are expensive and lack reliable evaluation of difficulty scores. |
| Approach: | They propose a flexible adaptive curriculum learning framework that integrates curriculum learning with a generic global curriculum. |
| Outcome: | The proposed framework improves learning performance and efficiency on three public dialogue datasets. |
LongT5: Efficient Text-To-Text Transformer for Long Sequences (2022.findings-naacl)
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| Challenge: | Recent work has shown that increasing the input length or increasing model size can improve the performance of Transformer-based neural models. |
| Approach: | They propose a model that integrates attention ideas from long-input transformers and adopts pre-training strategies from summarization pre-train into the scalable T5 architecture. |
| Outcome: | The proposed model outperforms the original T5 models on several summarization and question answering tasks and achieves state-of-the-art results. |
Challenging America: Modeling language in longer time scales (2022.findings-naacl)
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Jakub Pokrywka, Filip Graliński, Krzysztof Jassem, Karol Kaczmarek, Krzysztof Jurkiewicz, Piotr Wierzchon
| Challenge: | a dominant approach to solving NLP tasks is pre-training a large neural language model and fine-tuning the model for specific tasks. |
| Approach: | They propose a challenge to train and fine-tune large Transformer models for historical texts . they pre-trained a RoBERTa model from scratch from the historical texts and evaluate them on benchmarks . |
| Outcome: | The proposed ML task is based on OCR-ed clippings from the Chronicling America portal. |
LM-CORE: Language Models with Contextually Relevant External Knowledge (2022.findings-naacl)
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| Challenge: | Large pre-trained language models can capture factual knowledge in their parameters but storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of information and resource requirements. |
| Approach: | They propose a framework that provides explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art knowledge-enhanced language models on knowledge probing tasks and can handle knowledge updates. |
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are often used to achieve state-of-the-art results . eval paper shows that generative language model can handle joint and multi-task settings . |
| Approach: | They propose to reformulate extraction and prediction tasks into a sequence generation task . they propose a generative language model with unidirectional attention that learns to accomplish the tasks via language generation . |
| Outcome: | The proposed model outperforms the state-of-the-art in few-shot and full-shot settings. |
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for encoding text in tables require additional training and require additional pretraining. |
| Approach: | They propose a novel encoding strategy that preserves the critical property of permutation invariance across rows or columns. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three table interpretation tasks: column type annotation, relation extraction, and entity linking. |
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
| Outcome: | The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set. |
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents (2022.findings-naacl)
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Haeju Lee, Oh Joon Kwon, Yunseon Choi, Minho Park, Ran Han, Yoonhyung Kim, Jinhyeon Kim, Youngjune Lee, Haebin Shin, Kangwook Lee, Kee-Eung Kim
| Challenge: | Situated Interactive Multi-Modal Conversations 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs. |
| Approach: | They propose a joint learning approach that integrates visual inputs and performs all four subtasks at once for efficiency. |
| Outcome: | The proposed approach won the 10th Dialog Systems Technology Challenge (DSTC10) . it incorporates visual inputs and performs all four subtasks at once for efficiency . |
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment (2022.findings-naacl)
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| Challenge: | Existing models suffer performance degradation when evaluated on Spider-CG, even though every sub-sentence is seen during training. |
| Approach: | They propose a clause-level compositional example generation method to generate compositional biases from SQL clauses. |
| Outcome: | The proposed method improves generalization performance even on a training dataset. |
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems (2022.findings-naacl)
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| Challenge: | Existing models for persuasive dialogue lack emotion annotated data, so we use transformers to provide emotion based feedbacks to our RL agent. |
| Approach: | They propose to use a language model to generate empathetic persuasive dialogues . they annotate existing data with emotions and build transformers to provide feedbacks based on emotion. |
| Outcome: | The proposed model increases the rate of generating persuasive responses compared to state-of-the-art models while maintaining the language quality. |
Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU (2022.findings-naacl)
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| Challenge: | a recent study has shown that fine-tuning pre-trained models is parameter-inefficient and expensive. |
| Approach: | They propose a task-attuned token module which integrates pre-trained network representations into a pre-trainer. |
| Outcome: | The proposed model trains only 0.0009% of the parameters and is efficient during computation and scalable during deployment. |
The Limits of Word Level Differential Privacy (2022.findings-naacl)
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| Challenge: | Existing methods to anonymize textual data have several shortcomings . authors show that they can overcome these weaknesses and offer a formal privacy guarantee . |
| Approach: | They propose a method that circumvents most of the identified weaknesses and offers a formal privacy guarantee. |
| Outcome: | The proposed method outperforms the proposed methods in thourough experimentation and shows superior performance. |
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune a model for multiple tasks require a large amount of memory and computing power. |
| Approach: | They propose to factorize the weighs of a pre-trained Transformer model to improve training efficiency across multiple tasks by using BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark. |
| Outcome: | The proposed method matches or improves the original fine-tuned model’s performance for each task while effectively decreasing parameter requirements by two orders of magnitude. |
Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)
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| Challenge: | Prior work has referred to extractive (part of document) or abstractive (not part of document). |
| Approach: | They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings. |
| Outcome: | The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks. |
Improving Contextual Representation with Gloss Regularized Pre-training (2022.findings-naacl)
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| Challenge: | Experimental results show that the gloss regularizer module enhances word semantic similarity in pre-training. |
| Approach: | They propose an auxiliary gloss regularizer module to BERT pre-training to enhance word semantic similarity. |
| Outcome: | The proposed model improves word similarity in word-level and sentence-level representation. |
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models (2022.findings-naacl)
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| Challenge: | Pretrained language models (PLMs) have been shown to encapsulate social biases, including those relating to gender and race. |
| Approach: | They propose a new bias measure based on Jensen–Shannon divergence that retains more information from the model output probabilities than other previously proposed bias measures. |
| Outcome: | The proposed measure outperforms CrowS-Pairs and other similar measures for non-English datasets. |
Self-Training with Differentiable Teacher (2022.findings-naacl)
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| Challenge: | Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions. |
| Approach: | They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower. |
| Outcome: | The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks. |
SHARP: Search-Based Adversarial Attack for Structured Prediction (2022.findings-naacl)
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| Challenge: | SHARP is a new attack method for structured prediction models that solves several challenges. |
| Approach: | They propose a black-box adversarial attack method that uses a search-based optimization problem to attack adversarials. |
| Outcome: | The proposed method performs more potent attack than pioneer arts on two structured prediction tasks. |
MM-Claims: A Dataset for Multimodal Claim Detection in Social Media (2022.findings-naacl)
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Gullal Singh Cheema, Sherzod Hakimov, Abdul Sittar, Eric Müller-Budack, Christian Otto, Ralph Ewerth
| Challenge: | Using image and text, we investigate the role of image and texts in fake news detection . claim detection is a step in fighting misinformation and as a precursor to prioritize potentially false information for fact-checking. |
| Approach: | They propose a dataset that consists of tweets and corresponding images for claim detection . they evaluate strong unimodal and multimodal baselines and analyze drawbacks of current models . |
| Outcome: | The proposed dataset evaluates strong unimodal and multimodal baselines and examines drawbacks of existing models. |
QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning (2022.findings-naacl)
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| Challenge: | Synthetic datasets have been used to test visual question-answering datasets for reasoning abilities. |
| Approach: | They propose a visual question-answering dataset that is minimally biased and diagnostic . they propose to use the dataset to test visual reasoning abilities . |
| Outcome: | The proposed dataset is compared with existing models and shows it is far superior to existing models. |
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)
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| Challenge: | Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning. |
| Approach: | They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma. |
| Outcome: | The proposed model can solve MWP problems on English and Chinese benchmarks. |
Restoring Hebrew Diacritics Without a Dictionary (2022.findings-naacl)
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| Challenge: | a number of modern Hebrew texts are written in a letter-only version of the Hebrew script, which omits the diacritics present in the full diacritized, or dotted variant. |
| Approach: | They propose a character-level LSTM that can accurately diacritize Hebrew script without human-curated resources. |
| Outcome: | The proposed model performs on par with complex curation-dependent systems across a diverse array of modern Hebrew sources. |
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking (2022.findings-naacl)
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| Challenge: | Abstractive summarization systems generate paraphrases, but they often contain information inconsistent with the source text. |
| Approach: | They propose to generate factually inconsistent summaries using source texts and reference summary with key information masked to train a factual consistency classifier. |
| Outcome: | The proposed method outperforms existing models and shows a competitive correlation with human judgments. |
Probing the Role of Positional Information in Vision-Language Models (2022.findings-naacl)
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| Challenge: | Pre-trained Vision-Language models can't be used to understand image structure by injecting position information (PI) about objects in the image. |
| Approach: | They propose two strategies to probe the use of PI in Vision-Language models and investigate their effect on Visual Question Answering. |
| Outcome: | The proposed model can correctly classify if images with detailed PI statements match. |
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer (2022.findings-naacl)
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| Challenge: | Existing approaches for few-shot transfer show significant gain over zero-shot transfers . language resource distribution is skewed across the world's languages . proposed methods use multiple measures such as data entropy and gradient embedding . |
| Approach: | They propose a loss embedding method for sequence labeling tasks that induces diversity and uncertainty sampling similar to gradient embeddment. |
| Outcome: | The proposed methods outperform baseline methods for POS tagging, NER, and NLI tasks for up to 20 languages. |
A Self-supervised Joint Training Framework for Document Reranking (2022.findings-naacl)
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| Challenge: | Pretrained language models have been successfully applied to a wide range of tasks . however, the pretraining tasks were based on the context of documents . |
| Approach: | They propose a self-supervised joint training framework with a method called Masked Query Prediction to establish semantic relations between given queries and positive documents. |
| Outcome: | The proposed framework outperforms existing models on document reranking tasks without further pre-training . it uses a self-supervised method to establish semantic relations between given queries and positive documents. |
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)
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| Challenge: | Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors. |
| Approach: | They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training. |
| Outcome: | The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets. |
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning (2022.findings-naacl)
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| Challenge: | Pre-trained language models (PLMs) are a good starting point for downstream applications, but it is difficult to generalize them to new tasks given a few labeled samples. |
| Approach: | They propose to use Relation Graph augmented learning to improve the performance of few-shot natural language understanding tasks by rewriting the input sequence into a cloze question with masks. |
| Outcome: | Extensive experiments show that Relation Graph augmented learning (RGL) improves performance of prompt-based tuning strategies. |
Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering (2022.findings-naacl)
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| Challenge: | Existing methods for Knowledge Base Question Answering rely on semantic parsing and information retrieval. |
| Approach: | They propose a contrastive regularization based method to extract correct answer entities from a context knowledge base and a corresponding question. |
| Outcome: | The proposed method achieves state-of-the-art performance on the WebQuestionsSP dataset and the effectiveness of proposed modules is also evaluated. |
Phrase-level Textual Adversarial Attack with Label Preservation (2022.findings-naacl)
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| Challenge: | Existing adversarial attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality. |
| Approach: | They propose a phrase-level perturbation-based adversarial ATtack that generates adversarials through phrase- level perturbations. |
| Outcome: | The proposed approach improves the performance of natural language processing models by reducing the need for word-level perturbations and preserving the fluency and grammaticality of the samples. |
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022.findings-naacl)
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| Challenge: | Existing methods to identify all possible user intents at design time are expensive and require storage of past data. |
| Approach: | They propose to continually train an intent detector on new intents while maintaining performance on prior intents. |
| Outcome: | The proposed method outperforms exemplar replay-based approaches on lifelong intent detection tasks and achieves state-of-the-art on four public datasets. |
OTExtSum: Extractive Text Summarisation with Optimal Transport (2022.findings-naacl)
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| Challenge: | Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. |
| Approach: | They propose to formulate extractive text summarisation as an Optimal Transport (OT) problem and use it to obtain an optimal summary that minimises the transportation cost to a given document. |
| Outcome: | The proposed method outperforms state-of-the-art methods and learning-based methods on multiNews, PubMed, BillSum, and CNN/DM datasets. |
Speeding Up Entmax (2022.findings-naacl)
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| Challenge: | Recent studies suggest that sparsity is a problem when the trained model is used for inference. |
| Approach: | They propose an alternative to softmax that produces a dense probability distribution but is slower than softmax. |
| Outcome: | The proposed method keeps its virtuous characteristics but is slower than softmax and achieves on par or better performance in machine translation task. |
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (2022.findings-naacl)
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| Challenge: | Code-switching dependency parsing is a challenging task due to the scarcity of necessary resources and structural difficulties embedded in code-switch languages. |
| Approach: | They propose to use sequence labeling models as auxiliary tasks for code-switched dependency parsing in a semi-supervised scheme and acquire state-of-the-art scores on all studied languages. |
| Outcome: | The proposed model outperforms the previous model by 7.4 LAS points on average on all of the studied languages. |
Dangling-Aware Entity Alignment with Mixed High-Order Proximities (2022.findings-naacl)
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Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen
| Challenge: | Existing methods for dangling-aware entity alignment are underexplored but important problem. |
| Approach: | They propose a framework that uses high-order proximities to detect dangling entities and align matchable entities. |
| Outcome: | The proposed framework detects dangling entities and aligns matchable entities better than existing methods. |
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks (2022.findings-naacl)
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| Challenge: | Experimental results show that Transformer Encoder model can't automatically capture word order, so explicit position embeddings are required to be fed into the target model. |
| Approach: | They propose a Transformer-based language model DecBERT that uses a causal attention mask to capture word order. |
| Outcome: | The proposed model improves on the GLUE language understanding benchmark and accelerates the pre-training process. |
Towards Computationally Feasible Deep Active Learning (2022.findings-naacl)
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Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov
| Challenge: | Active learning (AL) is a technique for reducing the amount of annotation required for training machine learning models. |
| Approach: | They propose two techniques that reduce the amount of time required for AL . they use pseudo-labeling and distilled models to train a successor model . |
| Outcome: | The proposed algorithm reduces the time and computational overhead required to train an acquisition model and estimate uncertainty on instances in the unlabeled pool. |
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)
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| Challenge: | Existing methods for conversational question answering significantly degrade on datasets . a new task aims to enable systems to model complex dialogues flow given the speech documents . |
| Approach: | They propose a new Spoken Conversational Question Answering task to model human conversations . they propose DDNet, which ingests cross-modal information to achieve fine-grained representations of speech and language modalities. |
| Outcome: | The proposed method achieves superior performance in spoken conversational question answering. |
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)
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| Challenge: | Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated. |
| Approach: | They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. |
| Outcome: | The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable. |
FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems (2022.findings-naacl)
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| Challenge: | Linguistic bias in Deep Neural Network (DNN) based systems is a critical challenge that needs attention. |
| Approach: | They propose to integrate a lightweight embedding with existing NLP systems to mitigate linguistic bias without adaptation. |
| Outcome: | The proposed framework reduces linguistic bias and enhances usability of baselines for twelve languages. |
A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
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| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
| Approach: | They examine the relationship between stance detection and mis- and disinformation detection online and examine the results of previous studies. |
| Outcome: | The proposed task is a component of fact-checking, rumour detection, and detecting previously fact- checked claims, and is compared with other related tasks such as argumentation mining and sentiment analysis. |
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)
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| Challenge: | Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization. |
| Approach: | They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context. |
| Outcome: | The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency. |
To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing models fail to recognize answerable questions due to subtle literal changes . MRC models are forced to perceive crucial semantic changes from slight literal differences. |
| Approach: | They propose a span-based method of Contrastive Learning which explicitly contrasts answerable questions with their answerable counterparts at the answer span level. |
| Outcome: | The proposed method improves baselines significantly and is an effective way to utilize generated questions. |
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing methods for target-guided response generation are inconsistent with human judgement ratings. |
| Approach: | They propose a technique that finds a bridging path between the source and target and uses it to generate transition responses. |
| Outcome: | The proposed technique outperforms baselines on target-guided response generation task. |
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla (2022.findings-naacl)
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Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | Bangla is a widely spoken yet low-resource language in the NLP literature. |
| Approach: | They propose a BERT-based natural language understanding model pretrainable in Bangla, a widely spoken yet low-resource language in the NLP literature. |
| Outcome: | The proposed model outperforms multilingual and monolingual models on four NLU tasks covering text classification, sequence labeling, and span prediction. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text (2022.findings-naacl)
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| Challenge: | Existing benchmarks for entity set expansion (ESE) are limited to well-formed text and well-defined concepts. |
| Approach: | They propose to use user-generated text to assess the generalizability of ESE methods by identifying phenomena such as non-named entities, multifaceted entities and vague concepts. |
| Outcome: | The proposed methods are based on user-generated text to assess their generalizability and performance. |
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection (2022.findings-naacl)
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| Challenge: | a lack of general-purpose tools to characterize and predict ideology across genres of text remains a challenge . a recent study compared ideology-driven pretraining tasks with long or formal written texts . |
| Approach: | They propose to use a large-scale dataset to train pretraining models that compare political news articles on the same story written by different ideologies. |
| Outcome: | The proposed model outperforms baseline models and state-of-the-art models on ideology prediction and stance detection tasks. |
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune pre-trained language models are parameter efficient . fine- tuning the models requires multiple copies of the parameters, which is inefficient. |
| Approach: | They propose to use kernel-based adapters to tune only a few parameters while freezing the rest of the parameters. |
| Outcome: | The proposed methods achieve or improve strong performance over a diverse set of natural language generation and understanding tasks. |
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation (2022.findings-naacl)
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Md Akmal Haidar, Nithin Anchuri, Mehdi Rezagholizadeh, Abbas Ghaddar, Philippe Langlais, Pascal Poupart
| Challenge: | Existing methods for intermediate layer knowledge distillation suffer from computational burdens and engineering efforts for setting up a proper layer mapping. |
| Approach: | They propose a method where intermediate layers from teacher and student models are randomly selected to be distilled into intermediate layers of student models. |
| Outcome: | The proposed method outperforms state-of-the-art intermediate layer knowledge distillation methods on GLUE tasks and out-of domain test sets. |
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias (2022.findings-naacl)
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| Challenge: | Existing solvers with data bias and learning bias only learn shallow heuristics rather than deep semantics for understanding problems. |
| Approach: | They propose a MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in collected data and annotating them manually. |
| Outcome: | The proposed dataset has significantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers’ reasoning skills rather than matching nearest neighbors. |
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation (2022.findings-naacl)
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| Challenge: | Recent studies have shown that the effective use of contextual information between sentences can achieve better performance in document-level machine translation. |
| Approach: | They propose a recurrent memory unit to the Transformer to support the information exchange between the sentence and previous context. |
| Outcome: | The proposed model outperforms the previous work on TED and News by 0.91 s-BLEU and 1.49 d-BLUE on average. |
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions (2022.findings-naacl)
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| Challenge: | Humans can efficiently learn about new concepts from language descriptions, and we propose a new machine learning model, LIDE, which has a text decoder to generate the descriptions and a decoded text encoder to obtain the text representations of machine-generated descriptions. |
| Approach: | They propose a model with a text decoder to generate the descriptions and a corresponding text encoder to obtain the text representations of machine- or user-generated descriptions. |
| Outcome: | The proposed model outperforms baseline models with machine-generated descriptions and with high-quality user-generated models with high quality explanations. |
All Information is Valuable: Question Matching over Full Information Transmission Network (2022.findings-naacl)
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| Challenge: | Existing methods for question matching only transmit one kind of information while failing to utilize both kinds of information simultaneously. |
| Approach: | They propose a question matching network that can transmit both representation and interactive information together in a simultaneous fashion. |
| Outcome: | The proposed approach outperforms strong baseline models on two standard benchmarks. |
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation (2022.findings-naacl)
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| Challenge: | Neural text generation is a novel technique to describe biomedical pathways without manually curation. |
| Approach: | They propose a new dataset Pathway2Text which contains 2,367 pairs of biomedical pathways and textual descriptions. |
| Outcome: | The proposed method improves on both Graph2Text and Text2Graph tasks and can be used as a benchmark for biomedical named entity recognition. |
Exploring Neural Models for Query-Focused Summarization (2022.findings-naacl)
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| Challenge: | Recent work in Query-focused summarization lacks a comprehensive study of the broad space of applicable modeling methods. |
| Approach: | They propose to explore two general classes of methods for Query-focused summarization: extractive-abstractive solutions and end-to-end models. |
| Outcome: | The proposed models achieve state-of-the-art on the QMSum dataset, with a margin of 3.38 ROUGE-1, 3.72 ROUGe2 and 3.28 ROUGEL-L. |
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation (2022.findings-naacl)
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| Challenge: | Existing methods to improve Neural Machine Translation (NMT) for lowresource languages are often trained on heuristically aligned or automatically mined data. |
| Approach: | They propose to filter out imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT) instead, they propose to refine mined bitexts by automatic editing . |
| Outcome: | The proposed method improves the quality of mined bitexts for low-resource languages by up to 8 BLEU points. |
MixQG: Neural Question Generation with Mixed Answer Types (2022.findings-naacl)
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| Challenge: | Existing neural question generation approaches focus on short factoid type of answers. |
| Approach: | They propose a neural question generator that trains a single generative model by combining multiple question types with different answer types. |
| Outcome: | The proposed model outperforms existing models in both seen and unseen domains and can generate questions with different cognitive levels when conditioned on different answer types. |
Temporal Attention for Language Models (2022.findings-naacl)
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| Challenge: | Pretrained language models are trained on corpora derived from the web, but ignore this information. |
| Approach: | They propose a time-aware self-attention mechanism that captures time-specific contextualized word representations and allows the transformer to capture this information. |
| Outcome: | The proposed model achieves state-of-the-art on three datasets in different languages (English, German, and Latin) that vary in time, size, and genre. |
Efficient Few-Shot Fine-Tuning for Opinion Summarization (2022.findings-naacl)
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| Challenge: | Abstractive summarization models are typically pre-trained on large amounts of generic texts . large annotated datasets of reviews paired with reference summaries are not available . |
| Approach: | They propose a few-shot method which uses adapters to store in-domain knowledge . they pre-train adapters on unannotated customer reviews and fine-tune them on annotated datasets . |
| Outcome: | The proposed method can store in-domain knowledge and improves on large annotated reviews . it improves coherence and redundancies on the Amazon and Yelp datasets . |
Domain-matched Pre-training Tasks for Dense Retrieval (2022.findings-naacl)
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Barlas Oguz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Scott Yih, Sonal Gupta, Yashar Mehdad
| Challenge: | Existing approaches to improve performance of pre-training tasks are needed. |
| Approach: | They propose to pre-train large bi-encoder models on a recently released set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting reddit conversation dataset. |
| Outcome: | The proposed model can be pre-trained on a set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting dataset of Reddit conversations. |
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)
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Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
| Challenge: | a recent study aims to answer factual questions using a structured knowledge base (KBQA). |
| Approach: | They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge. |
| Outcome: | The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods. |
White-box Testing of NLP models with Mask Neuron Coverage (2022.findings-naacl)
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| Challenge: | Recent research has shown that black-box testing is not applicable to NLP models. |
| Approach: | They propose a set of white-box testing methods that are customized for transformer-based NLP models and adapt them to a black-box test suite. |
| Outcome: | The proposed methods can reduce testing suites by 60% while retaining failing tests, thereby concentrating faultdetection power of the test suite. |
Hierarchical Transformers Are More Efficient Language Models (2022.findings-naacl)
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Piotr Nawrot, Szymon Tworkowski, Michał Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski
| Challenge: | Transformers are impressive but inefficient and costly, which limits their applications and accessibility. |
| Approach: | They first use different ways to downsample and upsamplify activations in Transformers to make them hierarchical. |
| Outcome: | The proposed model outperforms Transformers on the ImageNet32 and enwik8 benchmarks. |
DISARM: Detecting the Victims Targeted by Harmful Memes (2022.findings-naacl)
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| Challenge: | DISARM is a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and then incorporates a novel contextualized deep neural network to classify whether the meme intends to harm these entities. |
| Approach: | They propose a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and incorporates a novel contextualized deep neural network to classify whether the meme intends to harm them. |
| Outcome: | The proposed framework outperforms 10 unimodal and multimodal systems and reduces error rate of harmful target identification by 9 % absolute over baseline systems. |
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation (2022.findings-naacl)
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| Challenge: | Existing vision-and-language pretraining approaches rely on external object detectors to encode images in a multi-modal transformer framework. |
| Approach: | They propose an object-aware end-to-end VLP framework which feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. |
| Outcome: | The proposed framework achieves competitive or superior performances on vision-language tasks. |
Dependency Position Encoding for Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing methods to extract relation extraction from sentence are limited in focusing on leveraging dependency information. |
| Approach: | They propose dependency position encoding (DPE) that incorporates dependency connections and dependency types into the self-attention mechanism to distinguish the importance of different word dependencies. |
| Outcome: | The proposed method significantly outperforms the previous methods on SemEval 2010 Task 8, KBP37, and TACRED. |
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)
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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
| Challenge: | Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts. |
| Approach: | They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets. |
The Role of Context in Detecting Previously Fact-Checked Claims (2022.findings-naacl)
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| Challenge: | Recent years have seen the proliferation of disinformation and fake news online. |
| Approach: | They propose to model the context of a political debate and the contexts of the document describing the fact-checked claim. |
| Outcome: | The proposed model improves on the state-of-the-art model by modeling the context of the claim . the experimental results show that the model can provide 10+ points of improvement over the state of the art model . |
Pruning Adatperfusion with Lottery Ticket Hypothesis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are computationally expensive to fine-tune and require large storage. |
| Approach: | They propose a method to identify the influence of each adapter module and a way to prune adapters based on the Lottery Ticket Hypothesis. |
| Outcome: | The proposed model reduces size significantly while keeping performance intact. |
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification (2022.findings-naacl)
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| Challenge: | Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services . e-learning systems should be able to enrol, identify, and verify new and recurring users based on their personal information . |
| Approach: | They propose to formalise three authentication tasks and their evaluation protocols . they propose to use a spoken multilingual dataset with 5,506 spoken dialogues . |
| Outcome: | The proposed models set the first competitive benchmarks and set directions for future research. |
Post-Training Dialogue Summarization using Pseudo-Paraphrasing (2022.findings-naacl)
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| Challenge: | Existing approaches to dialogue summarization use dialogue-specific features that require additional knowledge to recognize or make the models harder to tune. |
| Approach: | They propose to post-train pretrained language models to rephrase from dialogue to narratives and fine-tune them as usual. |
| Outcome: | The proposed approach outperforms existing models by summary quality and implementation costs. |
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022.findings-naacl)
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| Challenge: | Sarcasm employs ambivalence, where one says something positive but actually means negative . linguistically, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit . |
| Approach: | They propose a Dual-Channel Framework to model literal and implied sentiments separately . they propose sarcastic networks that can detect sarcasm sentiments in political debates . |
| Outcome: | The proposed framework achieves state-of-the-art on political debates and Twitter datasets. |
Zero-shot Entity Linking with Less Data (2022.findings-naacl)
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G P Shrivatsa Bhargav, Dinesh Khandelwal, Saswati Dana, Dinesh Garg, Pavan Kapanipathi, Salim Roukos, Alexander Gray, L Venkata Subramaniam
| Challenge: | Entity linking maps an entity mention in a natural language sentence to an entity in KB. |
| Approach: | They propose a neuro-symbolic, multi-task learning approach to bridge this gap by exploiting an auxiliary information about entity types. |
| Outcome: | The proposed approach achieves significantly higher performance on four different benchmark datasets when trained with just 0.01%, 0.1%, or 1% of the training data. |
GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing work only encodes entity types and textual context within individual instances, which limits the performance of sentence-level relation extraction (RE). |
| Approach: | They propose a module that aggregates the features from sentences to learn global representations of properties and augments local features within individual sentences. |
| Outcome: | The proposed module can learn global representations of properties from sentences and augment local features within individual sentences. |
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)
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| Challenge: | Existing approaches to generative commonsense reasoning hypothesize that pre-trained models lack sufficient parametric knowledge for this task. |
| Approach: | They propose to use order-agnostic input to elaborately manipulate the order of the given concepts before generation to evaluate their commonsense knowledge. |
| Outcome: | The proposed approach outperforms more sophisticated models with a lot of external data and resources in the task of generating a logical sentence from a set of concepts. |
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models (2022.findings-naacl)
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| Challenge: | Existing work identifies task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts. |
| Approach: | They propose to automatically identify spurious correlations in NLP models at scale by using existing interpretability methods to extract tokens that significantly affect model’s decision process. |
| Outcome: | The proposed method can identify spurious correlations in NLP models at scale and mitigate these leads to more robust models in multiple applications. |
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models (2022.findings-naacl)
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| Challenge: | Existing approaches to enhance pre-trained language models (PTMs) with a knowledge-aware graph neural network (GNN) encoder that models a commonsense knowledge graph (CSKG) can't explain how external knowledge resources improve the reasoning capacity of PTMs. |
| Approach: | They propose to use relation features from CSKGs to enhance the reasoning capacity of pre-trained language models (PTMs) by encoding a commonsense knowledge graph (CSKG) |
| Outcome: | The proposed approach reduces the parameters for encoding CSKGs and improves on five benchmarks. |
Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)
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| Challenge: | Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints. |
| Approach: | They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media. |
| Outcome: | The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method. |
Detecting Narrative Elements in Informational Text (2022.findings-naacl)
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| Challenge: | Recent work has focused on identifying narrative elements in personal stories texts, but this paper focuses on informational texts. |
| Approach: | They propose a novel NLP task for detecting narrative elements in raw text by adapting elements from the oral narrative theory of Labov and Waletzky and adding a new narrative element of their own. |
| Outcome: | The proposed scheme achieves an average F1 score of 0.77 and is better suited for informational texts than the oral narrative theory. |
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? (2022.findings-naacl)
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| Challenge: | Existing methods to improve pre-training for many-to-many neural machine translation use manual cleaning of bilingual dictionaries, which are unavailable for most language pairs. |
| Approach: | They propose a word-level contrastive objective to leverage word alignments for many-to-many neural machine translation (NMT) Empirical results show that this leads to 0.8 BLEU gains for several language pairs. |
| Outcome: | Empirical results show that the proposed objective leads to 0.8 BLEU gains for several language pairs. |
Minimally-Supervised Relation Induction from Pre-trained Language Model (2022.findings-naacl)
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| Challenge: | Existing methods to induce relation in NLP depend heavily on word embeddings. |
| Approach: | They propose a method to induce relation with BERT under minimal supervision . they first extract proper templates from corpus and then use BERT attention weights to represent the pseudo-sentences. |
| Outcome: | The proposed method achieves state-of-the-art in relation induction tasks on Google Analogy Test Sets, Bigger Analogy test set (BATS) and DiffVec. |
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)
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| Challenge: | Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline . |
| Approach: | They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs. |
| Outcome: | Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient. |
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm (2022.findings-naacl)
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| Challenge: | Prompt-based learning inherits the vulnerability from pre-training, where model predictions can be misled by inserting triggers into the text. |
| Approach: | They propose a potential solution to mitigate this vulnerability by injecting triggers into pre-trained language models using only plain text. |
| Outcome: | The proposed learning paradigm inherits the vulnerability from the pre-training stage . it can totally control or severely decrease the performance of prompt-based models . |
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering (2022.findings-naacl)
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| Challenge: | Existing numerical reasoning models overly rely on parametric knowledge at inference time . previous studies show that understanding numbers in text improves numerical reasoning accuracy . |
| Approach: | They propose a numerical reasoning model that leverages parametric knowledge to alleviate this over-reliance on parametric information. |
| Outcome: | The proposed model improves numerical reasoning accuracy and performance in DROP. |
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing methods for Few-shot Relation Extraction focus on implicitly introducing relation information to constrain the prototype representation learning. |
| Approach: | They propose a parameter-less method to promote few-shot relation extraction . they use a prototype rectification module to rectify original prototypes by relation information . |
| Outcome: | The proposed method achieves state-of-the-art on fewRel 1.0 and 2.0 datasets. |
HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea (2022.findings-naacl)
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| Challenge: | Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters. |
| Approach: | They present a dataset that includes tasks for attribution, topic classification, named entity recognition and summary retrieval for classical Hanja documents. |
| Outcome: | The proposed models improve on the Annals of the Joseon Dynasty and Diaries of the Royal Secretariats datasets. |
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2022.findings-naacl)
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| Challenge: | Using sketch-based slot filling, text-to-SQL models suffer from over-complexity . et al., e.al., and d.albert, dr., propose a novel method for text- to-Sql generation . |
| Approach: | They propose to train sequence-to-sequence model with Schema-aware Denoising . they propose a clause-sensitive execution guided (EG) decoding strategy . |
| Outcome: | The proposed method improves performance in schema linking and grammar correctness . it also establishes new state-of-the-art on the WikiSQL benchmark . |
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval (2022.findings-naacl)
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| Challenge: | Existing multilingual video corpus moment retrieval methods are based on a two-stream structure. |
| Approach: | They propose a multilingual video corpus moment retrieval task that uses a two-stream structure to generate a query-visual similarity and a subtitle stream exploits the query-subtitle similarity. |
| Outcome: | The proposed method improves accuracy on a large-scale video corpus moment retrieval dataset. |
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition (2022.findings-naacl)
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| Challenge: | Named entity recognition (NER) is a system for identifying text spans pertaining to specific entity types. |
| Approach: | They propose a method to investigate the regularity of Chinese NER's entity mentions by a regularity-aware module and a periodicity-gnostic module. |
| Outcome: | The proposed model significantly outperforms previous state-of-the-art methods on three benchmark datasets and a practical medical dataset. |
CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection (2022.findings-naacl)
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| Challenge: | Recent advances in NLP have often been used to mitigate the spread of hate speech and cyber-bullying on social networks. |
| Approach: | They propose a framework for hate speech detection using user-anchored self-supervision and contextual regularization to learn better representations of hateful content. |
| Outcome: | The proposed approach secures 1-12% improvement in test set metrics over best performing approaches on two types of tasks and multiple popular English language social networking datasets. |
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation (2022.findings-naacl)
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| Challenge: | Existing work on QA explanation proposes to explain the answers with entailment trees composed of multiple enlargement steps. |
| Approach: | They propose a Module-based Entailment Tree GENeration framework that has multiple modules and a reasoning controller. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the standard benchmark with only 9% of the parameters. |
A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation (2022.findings-naacl)
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| Challenge: | Existing methods for title generation are based on timestep aware sentence embeddings, but they are not effective for generating a title with appropriate information in the content. |
| Approach: | They propose a Timestep aware Sentence Embedding mechanism which refreshes the sentences’ embeddings with corresponding key words in different decoding timesteps. |
| Outcome: | The proposed framework outperforms existing methods on various title generation tasks and the evaluation scores are significantly higher than previous approaches. |
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback (2022.findings-naacl)
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Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Tien Le, Shahab Sabahi, Minh-Tien Nguyen, Hung Le
| Challenge: | a framework to train summarization models with preference feedback is proposed . human-in-the-loop (HITL) allows humans to actively participate in supervising AI systems . |
| Approach: | They propose a framework to train summarization models with preference feedback interactively. |
| Outcome: | The proposed framework improves ROUGE scores and sample-efficiency in active, few-shot and online settings. |
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction (2022.findings-naacl)
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| Challenge: | Temporal Expression Extraction (TEE) is essential for understanding time in natural language. |
| Approach: | They propose a framework for multilingual Temporal Expression Extraction that leverages pre-trained language models to prompt cross-language knowledge transfer from English to non-English languages. |
| Outcome: | The proposed framework outperforms the existing SOTA methods on French, Spanish, Portuguese, and Basque by large margins. |
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript (2022.findings-naacl)
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| Challenge: | a growing number of livestreaming videos provide useful knowledge with exceptional visual demonstrations. |
| Approach: | They propose a human-annotated corpus for punctuation restoration in livestreaming video transcripts . they show popular natural language processing tools underperform on sentence boundary detection . |
| Outcome: | The proposed dataset shows that natural language processing tools underperform on sentence boundary detection on livestreaming video transcripts. |
Event Detection for Suicide Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue . |
| Approach: | They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation . |
| Outcome: | The proposed dataset captures suicide actions and ideation, and general risk and protective factors. |
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models (2022.findings-naacl)
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| Challenge: | Recent studies show that the energy requirements of current NLP models are growing at a rapid, unsustainable pace. |
| Approach: | They investigate ways to measure energy usage and different hardware settings that can be tuned to reduce energy consumption for training and inference for language models. |
| Outcome: | The proposed techniques can reduce energy consumption for training and inference for language models. |
What kinds of errors do reference resolution models make and what can we learn from them? (2022.findings-naacl)
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| Challenge: | Referring resolution is the task of identifying the referent of a natural language expression. |
| Approach: | They propose a model that restores weakening of the spatial natural constraints on referring expressions by evaluating their performance on different datasets. |
| Outcome: | The proposed model shows improved performance on the most challenging kinds of referring expressions on different datasets. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | NLU++ provides a more challenging evaluation environment for dialogue NLU models . Typical ToD systems still rely on a modular design . |
| Approach: | They propose to use NLU++ to provide a more challenging evaluation environment for dialogue NLU models. |
| Outcome: | The proposed dataset improves existing datasets and provides a much more challenging evaluation environment for dialogue NLU models. |
Challenges in Generalization in Open Domain Question Answering (2022.findings-naacl)
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| Challenge: | Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. |
| Approach: | They introduce and annotate questions according to three categories that measure training set overlap, compositional generalization, and novel-entity generalization. |
| Outcome: | The proposed models perform better on established datasets and lower on comp-gen/novel-entity questions than on the full test set. |
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence (2022.findings-naacl)
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| Challenge: | Recent evidence shows that large-size pre-trained language models do not satisfy the logical negation property (LNP) However, their reliability is being challenged due to faulty behaviours and incomprehension on number-related representations. |
| Approach: | They propose a new intermediate training task to directly learn meaning text correspondence instead of relying on the distributional hypothesis. |
| Outcome: | The proposed approach outperforms previous models on 7 GLUE tasks and outperformed previous models. |
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer (2022.findings-naacl)
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| Challenge: | a lack of labeled data for low-resource languages leads to the need for effective cross-lingual transfer learning. |
| Approach: | They propose a mixed training method that trains on both source and target data with stochastic gradient surgery, a novel gradient-level optimization. |
| Outcome: | The proposed method outperforms current methods on all tasks and escapes overfitting issues. |
Learning to Execute Actions or Ask Clarification Questions (2022.findings-naacl)
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| Challenge: | Existing work on Minecraft Corpus Dataset only learns to execute instructions neglecting the importance of asking for clarifications. |
| Approach: | They propose to annotate all builder utterances into eight types, including clarification questions, and propose a builder agent model capable of determining when to ask or execute instructions. |
| Outcome: | The proposed model outperforms existing models on the collaborative building task with a substantial improvement. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
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| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
Learning Structural Information for Syntax-Controlled Paraphrase Generation (2022.findings-naacl)
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| Challenge: | Syntax-controlled paraphrase generation aims to produce paraphrase conform to given syntactic patterns. |
| Approach: | They propose a model that captures parent-child and sibling relations and a syntax encoder to capture alignment relations. |
| Outcome: | The proposed model achieves state-of-the-art in terms of semantic and syntactic quality on two popular benchmark datasets. |
Controllable Sentence Simplification via Operation Classification (2022.findings-naacl)
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| Challenge: | Sentence simplification involves a sentence being transformed into a simpler version of itself while preserving its core meaning. |
| Approach: | They propose a controllable-simplification model that tailors simplifications to four global operations . they propose to use a dataset to train highly accurate classification systems for these operations based on syntactic or discourse structure . |
| Outcome: | The proposed model outperforms both end-to-end and controllable approaches in sentence simplification tasks. |
Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation (2022.findings-naacl)
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| Challenge: | Existing studies on open-domain conversational systems are limited to single corpus training and evaluation. |
| Approach: | They propose a method which encodes each corpus through a unique corpus embedding and a new word-level importance weighting method that integrates DF to the loss function. |
| Outcome: | The proposed methods gain significant improvements on both automatic and human evaluation. |
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net (2022.findings-naacl)
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| Challenge: | Existing methods focus on attention mechanism, but they are not suitable for abstractive text summarization. |
| Approach: | They propose a siamese generative adversarial net for abstractive text summarization which preserves the main semantics of the source text and the target summary. |
| Outcome: | The proposed model can preserve the main semantics of the source text and target summary. |
Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning (2022.findings-naacl)
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| Challenge: | a lack of nodes in the job title representation graph hinders further analysis . a new approach to learn job title represents the job for short . |
| Approach: | They propose to embed nodes that improve the quality of job title representation . they construct a heterogeneous graph with job titles and tags . |
| Outcome: | The proposed method improves the quality of job title representation on two datasets. |
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering (2022.findings-naacl)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing approaches to cross-lingual question answering use sentence embedding to map documents and questions in multiple languages . a novel cross-linguistic approach to cross language-retrieval question answering is proposed . our method outperforms competitors in 19 out of 21 settings of CL-ReQA . |
| Approach: | They propose a cross-lingual language knowledge transfer framework for cross-linguistic question answering . they use a multilingual sentence embedding technique to create a linguistic embeddable space . |
| Outcome: | The proposed method outperforms current state-of-the-art methods in 19 out of 21 settings of CL-ReQA. |
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog (2022.findings-naacl)
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| Challenge: | Existing task-oriented dialog systems suffer from error propagation from inaccurate dialog states and responses. |
| Approach: | They propose a back reconstruction approach for end-to-end task-oriented dialog system . they use back reconstruction to reconstruct the original input context from the generated dialog states . |
| Outcome: | Extensive experiments on MultiWOZ 2.0 and CamRest676 show the effectiveness of the proposed model. |
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun
| Challenge: | Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks. |
| Approach: | They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies. |
| Outcome: | The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%. |
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations (2022.findings-naacl)
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| Challenge: | Recent work shows that deep learning models are sensitive to low-level correlations between simple features and specific output labels, leading to over-fitting and lack of generalization. |
| Approach: | They propose to eliminate single-word correlations altogether to mitigate this problem . they highlight several alternatives to dataset balancing to enhance contexts . |
| Outcome: | The proposed approach to balancing datasets is insufficient, the authors argue . they suggest enhancing datasets with richer contexts and abstaining from interaction . |
Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning (2022.findings-naacl)
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| Challenge: | Pretrained masked language models inherit a considerable amount of relational knowledge from the source corpora. |
| Approach: | They propose to specialize pretrained masked language models into relational models from the perspective of network pruning. |
| Outcome: | The proposed model can represent grounded commonsense relations at non-trivial sparsity while being generalizable . the proposed model improves on a wealth of NLP tasks, but we know little about how much knowledge it imparts . |
D2GCLF: Document-to-Graph Classifier for Legal Document Classification (2022.findings-naacl)
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| Challenge: | Existing methods learn latent representations for each document by considering the semantics and themes of the documents. |
| Approach: | They propose a document-to-graph classifier which extracts facts as relations between key participants in a law case and represents a legal document with four relation graphs. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on a real-world legal document dataset. |
A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
| Approach: | They propose a framework that borrows entity information from the source domain to enhance NER in the target domain. |
| Outcome: | The proposed model improves over the state-of-the-art model on several datasets. |
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation (2022.findings-naacl)
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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
| Challenge: | Korean pretrained language models struggle to generate short sentences with a given condition based on compositionality and commonsense reasoning. |
| Approach: | They propose a Korean text-generation dataset for Korean generative commonsense reasoning and language model evaluation using a semi-automatic dataset construction approach. |
| Outcome: | The proposed dataset is available at http://aihub.or.kr/opendata/korea-university. |
Improve Discourse Dependency Parsing with Contextualized Representations (2022.findings-naacl)
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| Challenge: | Existing studies show that discourse dependency analysis is easier when describing text units in a context-dependent way. |
| Approach: | They propose to use transformers to encode contextualized representations of units of different levels to capture information needed for discourse dependency analysis. |
| Outcome: | The proposed model outperforms traditional direct classification methods on English and Chinese datasets. |
LiST: Lite Prompted Self-training Makes Parameter-efficient Few-shot Learners (2022.findings-naacl)
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| Challenge: | LiST is an efficient method for fine-tuning large pre-trained language models in few-shot learning settings. |
| Approach: | They propose a method for efficient fine-tuning of large pre-trained language models in few-shot settings using self-training and meta-learning. |
| Outcome: | The proposed method outperforms GPT-3 in-context learning by 33% on few-shot tasks. |
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection (2022.findings-naacl)
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| Challenge: | Existing methods for multimodal sentiment detection do not consider token-level feature fusion. |
| Approach: | They propose a method for multimodal sentiment detection using a combination of text and image to encode and fuse token-level features. |
| Outcome: | The proposed method can fuse multimodal features with token-level features on three publicly available multimodal datasets. |
Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)
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| Challenge: | Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive. |
| Approach: | They propose to query a masked language model with cloze style prompts to obtain supervision signals. |
| Outcome: | The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%. |
Analytical Reasoning of Text (2022.findings-naacl)
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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan
| Challenge: | Existing models with implicit reasoning ability struggle to solve analytical reasoning of text. |
| Approach: | They propose an approach to analyze text and use it to perform reasoning over it. |
| Outcome: | The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset. |
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback (2022.findings-naacl)
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| Challenge: | Existing work on news recommendation only used positive and negative implicit feedback and suffered from the noise impact. |
| Approach: | They propose a denoising neural network for news recommendation with positive and negative implicit feedback, named DRPN. |
| Outcome: | The proposed method improves on the real-world large-scale dataset. |
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation (2022.findings-naacl)
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| Challenge: | Existing stationary-trained MRC systems are usually trained with in-domain data but are applied to new domain data. |
| Approach: | They propose a continual machine reading comprehension model with uncertainty-aware fixed memory and adversarial domain adaptation that keeps a stable understanding by learning both memory and new domain data. |
| Outcome: | The proposed model is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings. |
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders (2022.findings-naacl)
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| Challenge: | Existing methods for abstractive summarization generate factual consistency summaries with a high level of accuracy and coherence. |
| Approach: | They propose a framework that induces the guidance information and generates summary equipment with the guidance synchronously. |
| Outcome: | The proposed framework generates fluent summaries with no constraint on the words and phrases, and is more faithful than the existing state-of-the-art approaches. |
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems (2022.findings-naacl)
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| Challenge: | Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. |
| Approach: | They propose a method to fine-tune language models in a goal-aware way . they evaluate a flight-booking method with a context-assisted language model . |
| Outcome: | The proposed method outperforms the state-of-the-art method on a flight-booking task by 7% in terms of task success. |
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity (2022.findings-naacl)
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| Challenge: | State-of-the-art open-domain dialogue models fail to maintain character identity throughout discourse . despite improvements in accuracy and self-contradiction, agents take on the role of interlocutor . |
| Approach: | They formalize and quantify the deficiency in character identity modeling by using human evaluations. |
| Outcome: | The proposed models reduce mistaken identity issues by nearly 65% according to human annotators while improving engagingness. |
Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training (2022.findings-naacl)
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| Challenge: | Existing question generation models require large-scale and high-quality training data. |
| Approach: | They propose an unsupervised domain adaptation approach to combat the lack of training data and domain shift issue with domain data selection and self-training. |
| Outcome: | The proposed approach outperforms baselines on three large datasets with different domain similarities, using a transformer-based pre-trained QG model. |
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2022.findings-naacl)
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| Challenge: | Existing approaches to answer open domain questions rely on unlabeled text or synthetically generated question-answer pairs. |
| Approach: | They propose a large-scale open-domain question-answering dataset based on the Common Crawl project that can be used to in-domain pre-train popular language models. |
| Outcome: | The proposed dataset achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks. |
The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank (2022.findings-naacl)
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| Challenge: | a natural language generation system can be used to create text at the end of a passage . fill in the blank (FITB) is a task of inserting text into a specified position in a text . |
| Approach: | They evaluate the feasibility of using a single model to perform both tasks . they show that models pre-trained with a FitB-style objective are capable of both tasks. |
| Outcome: | The proposed model can perform both fill in the blank and continuation tasks. |
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration (2022.findings-naacl)
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| Challenge: | Existing methods to extract relational facts without pre-defined relation types cluster hard or semi-hard instances into the same relation type. |
| Approach: | They propose a method to learn discriminative representations for open relation extraction by using instance ranking and label calibration strategies. |
| Outcome: | The proposed method outperforms existing methods on two public datasets. |
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (2022.findings-naacl)
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| Challenge: | Recent work shows that Relation Extraction tasks can be recasted as Textual Entailment tasks using verbalizations. |
| Approach: | They propose to recasted RE tasks as Textual Entailment tasks using verbalizations . they show that entailment reduces the need for manual annotation to 50% and 20% . |
| Outcome: | The proposed method reduces the need for manual annotation to 50% and 20% in event argument extraction tasks while achieving the same performance as with full training. |
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing approaches to extract relations require large-scale labeled data. |
| Approach: | They propose a Relation Contrastive Learning framework to mitigate similar relations and similar entities problems by optimizing a contrastive instance loss with a relation classification loss on seen relations. |
| Outcome: | The proposed framework can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously. |
Latent Group Dropout for Multilingual and Multidomain Machine Translation (2022.findings-naacl)
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| Challenge: | Multidomain and multilingual machine translation often rely on parameter sharing strategies, which are hardcoded in the network architecture, independent of the similarities between tasks. |
| Approach: | They propose a method to take advantage of similarities by using a latent-variable model and develop techniques to train this model end-to-end. |
| Outcome: | The proposed model improves translation performance without increasing the model size. |
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)
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| Challenge: | Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations. |
| Approach: | They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores. |
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing pre-trained MLMs produce an anisotropic distribution of token representations . this is not ideal for tasks that require discriminative semantic meanings of distinct tokens - a problem that exists in pre-training models . |
| Approach: | They propose a continual pre-training approach that encourages BERT to learn an isotropic distribution of token representations. |
| Outcome: | The proposed approach improves on a wide range of English and Chinese benchmarks. |
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)
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| Challenge: | Using MTG, we train and evaluate multilingual text generation models using human-annotated data. |
| Approach: | They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages. |
| Outcome: | The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios. |
Weakly Supervised Text-to-SQL Parsing through Question Decomposition (2022.findings-naacl)
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| Challenge: | Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. |
| Approach: | They propose a weak supervision approach for training text-to-SQL parsers by using a question meaning representation called QDMR to synthesize SQL queries from annotated NL-SqL data. |
| Outcome: | The proposed model performs competitively with those trained on annotated NL-SQL data. |
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing rumor detection methods are poor at detecting false rumors about breaking news or trending topics due to the lack of training data and prior knowledge. |
| Approach: | They propose an adversarial contrastive learning framework to detect false rumors by adapting features learned from well-resourced rumor data to that of the low-resource. |
| Outcome: | The proposed framework improves on two low-resource datasets and shows superior performance . it overcomes restriction of domain and/or language usage and improves robustness . |
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (2022.findings-naacl)
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| Challenge: | Recent research focused on knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. |
| Approach: | They propose a novel task-oriented dialogue system that effectively incorporates knowledge into a language model by using structural information of a knowledge graph. |
| Outcome: | The proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a Knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation. |
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (2022.findings-naacl)
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| Challenge: | Existing zero-shot event detection methods do not work for unseen types . supervised methods require predefined event types or external tools . |
| Approach: | They propose a framework to detect events from unstructured text without annotating samples . they propose to use ordered contrastive learning and prompt-based prediction to identify trigger words . |
| Outcome: | The proposed model detects events more effectively and accurately than state-of-the-art methods. |
KETOD: Knowledge-Enriched Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | Existing studies treat task-oriented dialogue and chit-chat as separate domains . a new dataset is created to integrate both types of dialogue into a single system . |
| Approach: | They propose to integrate task-oriented dialogue and knowledge-grounded chit-chat into a single model by using a dataset. |
| Outcome: | The proposed models improve the performance of knowledge-enriched dialogues while maintaining a competitive task-oriented dialog performance. |
TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization (2022.findings-naacl)
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| Challenge: | Existing pre-trained language models are difficult to apply to abstractive conversational summarization tasks. |
| Approach: | They propose a thread-aware Transformer-based network that incorporates contextual dependency into the conversational summarization model. |
| Outcome: | The proposed model can be applied to real conversations using a large-scale pretraining dataset. |
AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks (2022.findings-naacl)
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| Challenge: | Existing approaches to train transformers with millions of parameters require large storage. |
| Approach: | They propose a transformer-based adapter architecture that adds a token-dependent shift to the hidden output of transformer layers to adapt to downstream tasks with only a vector and a linear layer. |
| Outcome: | The proposed model significantly reduces trainable parameters with minimal performance loss compared to fine-tuned models. |
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
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| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
| Approach: | They propose a multi-candidate optimization framework for diverse NMT to deal with this defect. |
| Outcome: | The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. |
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer (2022.findings-naacl)
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| Challenge: | Text style transfer is an important task in controllable language generation due to the scarcity of large-scale parallel data. |
| Approach: | They propose a semi-supervised framework for text style transfer that bootstraps with supervision guided by automatically constructed pseudo-parallel pairs and improves the sequence-to-sequence policy gradient via reinforcement rewards. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple datasets and produces effective generation with as minimal as 10% of training data. |
EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction (2022.findings-naacl)
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| Challenge: | Recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events. |
| Approach: | They propose an event-aware argument extraction model with augmented context to improve consistency . they hypothesize that participants tend to play consistent roles across multiple events in a document . |
| Outcome: | The proposed model improves consistency and accuracy of arguments extracted from documents. |
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition (2022.findings-naacl)
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| Challenge: | Existing methods to locate and classify entities using knowledge bases and unlabeled corpus are expensive and limited application. |
| Approach: | They propose to use a method to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing them in contrastive learning frameworks. |
| Outcome: | The proposed method can give modified suggestions on distant data without additional supervised labels and thus reduces the requirement on the quality of the knowledge bases. |
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)
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| Challenge: | Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image. |
| Approach: | They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation. |
| Outcome: | The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
RoViST: Learning Robust Metrics for Visual Storytelling (2022.findings-naacl)
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| Challenge: | Visual storytelling is the task of generating a story paragraph that describes a given image sequence. |
| Approach: | They propose 3 evaluation metrics sets that analyze which aspects we would look for in a good story . they compare their correlation with human judgement scores on a sample of machine stories . |
| Outcome: | The proposed evaluation metrics outperform other metrics on human correlation on a sample of machine stories from state-of-the-art models. |
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings (2022.findings-naacl)
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| Challenge: | Existing methods to answer complex logical queries on incomplete knowledge graphs with missing edges are needed to solve the problem. |
| Approach: | They propose a query embedding method that encodes queries and entities to the same embeddable space and then selects the answer entities based on similarities . |
| Outcome: | The proposed method can answer complex logical queries on incomplete knowledge graphs with missing edges. |
ID10M: Idiom Identification in 10 Languages (2022.findings-naacl)
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| Challenge: | Identifying and understanding idioms in context is a key goal and challenge in Natural Language Understanding tasks. |
| Approach: | They propose a multilingual Transformer-based system for the identification of idioms and a manually-curated evaluation benchmark. |
| Outcome: | The proposed system performs well in 10 languages and is released on github. |
Cross-Domain Classification of Moral Values (2022.findings-naacl)
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| Challenge: | Existing methods to identify moral values in text can be challenging for transferring knowledge between domains. |
| Approach: | They compare a deep learning model with a domain-specific value classifier to find out whether it can transfer knowledge to new domains. |
| Outcome: | The proposed model can generalize and transfer knowledge to novel domains, but introduce catastrophic forgetting. |