Papers with natural language inference
Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
Rephrasing Invokes Better Generations for Large Language Models (2024.naacl-srw)
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| Challenge: | Existing methods for prompt tuning and input pre-processing are under-studied . e.g., ReLLM replaces low-frequency words with their high-frequency counterparts . |
| Approach: | They propose a method that automatically paraphrases input content for better output generation. |
| Outcome: | The proposed method is user-friendly and requires no additional training. |
Have Attention Heads in BERT Learned Constituency Grammar? (2021.eacl-srw)
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| Challenge: | Recent pre-trained language models have gained great success in many tasks, but what they have learned, and when they perform well remain unknown. |
| Approach: | They employ the syntactic distance method to extract implicit constituency grammar from attention weights of attention heads of BERT and RoBERTa. |
| Outcome: | The proposed models induce some grammar types much better than baselines, suggesting some heads act as a proxy for constituency grammar. |
When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs) (2023.starsem-1)
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| Challenge: | In this paper, we examine the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, and sentence fragments for natural language inference (NLI). |
| Approach: | They propose to fine-tune large language models to accommodate different sentence types for natural language inference (NLI) they also explore ChatGPT's concept of entailment by using a symbolic semantic parser. |
| Outcome: | The proposed models can accommodate different sentence types without losing too much accuracy on MNLI-matched models. |
Language Clustering for Multilingual Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Recent work in multilingual natural language processing has shown progress on tasks such as natural language inference and joint multilingual translation. |
| Approach: | They propose a technique that groups similar languages together by embeddings from a pre-trained masked language model and automatically discovering language clusters in this embeddable space. |
| Outcome: | The proposed technique outperforms baselines on 15 languages in the WikiAnn dataset showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba). |
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (D18-1)
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Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
| Challenge: | a plethora of new natural language inference datasets has been created in recent years . however, these datasets do not provide clear insight into what type of reasoning or inference a model may be performing. |
| Approach: | They propose to recast 13 existing natural language inference datasets into a common structure. |
| Outcome: | The proposed datasets provide insight into how well a sentence representation captures distinct types of reasoning. |
Natural Language Inference with Mixed Effects (2020.starsem-1)
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| Challenge: | aggregating raw annotations to a single label is problematic due to disagreement among annotators. |
| Approach: | They propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise. |
| Outcome: | The proposed method improves performance over models that do not incorporate such effects. |
Unsupervised Question Answering for Fact-Checking (D19-66)
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| Challenge: | Recent Deep Learning (DL) models have achieved human-level accuracy on natural language tasks such as question-answering, natural language inference, and textual entailment. |
| Approach: | They propose an unsupervised question-answering based approach for a similar task, fact-checking. |
| Outcome: | The proposed approach achieves label accuracy of 80.2% on the development set and 80.25% on the test set. |
Formal Semantic Controls over Language Models (2024.lrec-tutorials)
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| Challenge: | Text embeddings provide a concise representation of the semantics of sentences and larger spans of text, rather than individual words, capturing a wide range of linguistic features. |
| Approach: | They propose to shorten the gap between latent semantics and formal symbolics by comparing distributional models to symbolic models grounded on formal linguistics and well-defined mathematical properties. |
| Outcome: | This paper examines the analysis and control of text representations, covering methods from pooling to LLM-based. |
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization (2022.tacl-1)
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| Challenge: | Recent studies have shown that even state-of-the-art pre-trained language models can generate inconsistent summaries in more than 70% of all cases. |
| Approach: | They propose a method that enables NLI models to be used for inconsistency detection by segmenting documents into sentence units and aggregating scores between pairs of sentences. |
| Outcome: | The proposed method achieves state-of-the-art accuracy of 74.4% on six large inconsistency detection datasets. |
Paraphrase Identification via Textual Inference (2024.starsem-1)
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| Challenge: | Paraphrase identification (PI) and natural language inference (NLI) are important tasks in natural language processing. |
| Approach: | They propose a method for paraphrase identification and natural language inference using an NLI system to solve these tasks. |
| Outcome: | The proposed method outperforms dedicated PI models on PI datasets and provides insights into limitations of current benchmarks. |
A Hypothesis-Driven Framework for the Analysis of Self-Rationalising Models (2024.eacl-srw)
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| Challenge: | Recent advances in LLMs generating longer coherent text have popularised self-rationalising models, which produce a natural language explanation alongside their output. |
| Approach: | They propose a Bayesian network-based hypothesis-driven statistical framework that allows us to judge how similar LLM-generated free-text explanations are to LLMs. |
| Outcome: | The proposed framework does not exhibit a strong similarity to GPT-3.5. |
A Generative Approach for Mitigating Structural Biases in Natural Language Inference (2022.starsem-1)
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| Challenge: | Natural language inference datasets contain artifacts and biases that allow models to perform poorly by using a biased subset of the input without considering the remainder features. |
| Approach: | They reformulate a natural language inference task as a generative task . they find that this approach is highly robust to large amounts of bias . |
| Outcome: | The proposed model is highly robust to large amounts of bias. |
Annotation Artifacts in Natural Language Inference Data (N18-2)
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| Challenge: | Large-scale datasets for natural language inference are created by crowdsourcing annotations . authors show that success of natural language models to date has been overestimated . |
| Approach: | They propose a method for crowdsourcing annotations to generate 3 new sentences based on a sentence (premise) they show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI and 53% of MultiNLI . |
| Outcome: | The proposed model can classify the hypothesis alone in 67% of SNLI and 53% of MultiNLI datasets. |
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis (D19-3)
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| Challenge: | Legal Tech is a system that performs legal consulting, multi-way law searching, and legal document analysis using deep contextual representations and various attention mechanisms. |
| Approach: | They propose a Chinese legal system that performs legal consulting, multi-way law searching, and legal document analysis using deep contextual representations and various attention mechanisms. |
| Outcome: | The proposed system performs legal consulting, multi-way law searching, and legal document analysis by exploiting techniques such as deep contextual representations and various attention mechanisms. |
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Existing factual consistency metrics are often uncontrollably generating text that is factually inconsistent with inputs. |
| Approach: | They propose a weakly supervised framework that is directly trained on actual generated samples from language models with weakly annotated labels. |
| Outcome: | The proposed framework improves on the TRUE benchmark by 3.3% over existing methods with 435M parameters. |
OYXOY: A Modern NLP Test Suite for Modern Greek (2024.findings-eacl)
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Konstantinos Kogkalidis, Stergios Chatzikyriakidis, Eirini Giannikouri, Vasiliki Katsouli, Christina Klironomou, Christina Koula, Dimitris Papadakis, Thelka Pasparaki, Erofili Psaltaki, Efthymia Sakellariou, Charikleia Soupiona
| Challenge: | a select few languages claim the lion's share in the literature, boasting a plethora of models and a constant stream of results. |
| Approach: | They propose a linguistically motivated evaluation suite for Greek NLP . they propose linguistic-motivated tasks for natural language inference, word sense disambiguation and metaphor detection . |
| Outcome: | The proposed evaluation suite for Greek NLP is linguistically motivated and technically relevant. |
Calibration of Pre-trained Transformers (2020.emnlp-main)
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| Challenge: | Pre-trained Transformers dominate benchmark tasks but use a large number of self-attention heads across many layers in a way that is difficult to unpack. |
| Approach: | They analyze pre-trained Transformer models' posterior probabilities to determine whether they are calibrated for three tasks: natural language inference, paraphrase detection, and commonsense reasoning. |
| Outcome: | The models are calibrated in-domain and out-of-domain, and their calibration error out-domain can be as much as 3.5x lower. |
LINSPECTOR WEB: A Multilingual Probing Suite for Word Representations (D19-3)
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| Challenge: | LINSPECTOR WEB is an open source multilingual inspector to analyze word embeddings. |
| Approach: | They propose to use LINSPECTOR WEB to analyze word embeddings in 28 languages. |
| Outcome: | The system performs 16 simple linguistic probing tasks for a diverse set of 28 languages. |
Extractive NarrativeQA with Heuristic Pre-Training (D19-58)
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| Challenge: | Automated question answering (QA) from text remains a challenge for humans . a striking gap exists between machine and human performance on NLP tasks . |
| Approach: | They propose a heuristic extractive version of a data set to solve the problem of answer extraction rather than generation. |
| Outcome: | The proposed model outperforms previous models on summary-level QA from full narratives and on the METEOR metric. |
Character-Based Neural Networks for Sentence Pair Modeling (N18-2)
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| Challenge: | Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification and semantic textual similarity. |
| Approach: | They propose to use subwords to represent sentences without pretrained word embeddings . they find that subword models can achieve new state-of-the-art results without pretraining . |
| Outcome: | The proposed models can achieve state-of-the-art results on two social media datasets and competitive results on news data for paraphrase identification. |
Logographic Information Aids Learning Better Representations for Natural Language Inference (2022.findings-aacl)
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| Challenge: | Statistical language models often ignore information related to logographic features of written text . many languages, including Chinese and Vietnamese, use logographic writing systems . |
| Approach: | They propose to provide language models with logographic information to learn better semantic representations. |
| Outcome: | The proposed model improves semantic representations in six languages with logographic writing systems. |
On the Role of Summary Content Units in Text Summarization Evaluation (2024.naacl-short)
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Marcel Nawrath, Agnieszka Nowak, Tristan Ratz, Danilo Walenta, Juri Opitz, Leonardo Ribeiro, João Sedoc, Daniel Deutsch, Simon Mille, Yixin Liu, Sebastian Gehrmann, Lining Zhang, Saad Mahamood, Miruna Clinciu, Khyathi Chandu, Yufang Hou
| Challenge: | a human written summary content unit (SCU) is used to judge the quality of a summary . a pyramid evaluation method is based on SCUs that decompose a reference summary into concise sentences . |
| Approach: | They propose to use automated SCUs to evaluate the quality of a candidate summary . they propose to generate SCU approximations from AMR meaning representations and large language models . |
| Outcome: | The proposed method can be fully automated, but lacks the human effort to validate it. |
Augmenting Neural Networks with First-order Logic (P19-1)
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| Challenge: | Existing paradigms for training neural networks require large datasets, a paper argues . we present a framework for introducing declarative knowledge to neural networks . |
| Approach: | They propose a framework for introducing declarative knowledge to neural networks . they compile logical statements into graphs that augment a network without extra learnable parameters or manual redesign. |
| Outcome: | The proposed framework improves on three tasks, especially in low-data regimes. |
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models (2022.lrec-1)
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| Challenge: | Existing studies on NLP touch upon moral contexts in text. |
| Approach: | They construct a dataset that can be used for moral judgement tasks on a popular reddit subreddit. |
| Outcome: | The proposed model passes moral judgements on posts from a popular reddit subreddit . it shows that the model can be fine tuned and improves across the datasets . |
Discovering Better Model Architectures for Medical Query Understanding (2021.naacl-industry)
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| Challenge: | Neural architecture search (NAS) has attracted intense attention in computer vision and NLP. |
| Approach: | They propose to use neural architecture search to optimize model architectures for medical questions . they propose to modify the ENAS method to accelerate and stabilize the search results . |
| Outcome: | The proposed approach outperforms baseline models on two medical questions . it is compared with other NAS methods and shows that it provides the best results . |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
Improve Interpretability of Neural Networks via Sparse Contrastive Coding (2022.findings-emnlp)
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| Challenge: | XAI has achieved remarkable advances, but few efforts have been devoted to solving the problem. |
| Approach: | They propose a model-agnostic explanation method termed Sparse Contrastive Coding . they use model-based explanations to explain the black-box in a more model-oriented way . |
| Outcome: | The proposed method outperforms five state-of-the-art methods in interpretability and classification metrics. |
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation (2024.naacl-long)
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| Challenge: | Existing methods for evaluating factual consistency of abstractive summarization lack coherence or error-type coverage. |
| Approach: | They propose a framework that generates perturbed summaries using Abstract Meaning Representations (AMRs) they use a selection module NegFilter to ensure the quality of the generated negative examples . |
| Outcome: | The proposed framework outperforms existing systems on the AggreFact-SOTA benchmark and provides high error-type coverage. |
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification (2024.acl-long)
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| Challenge: | Existing annotation codebook is labor-intensive for coding events from large datasets. |
| Approach: | They propose to use existing annotation codebook to classify political relations without extensive annotations. |
| Outcome: | The proposed methods outperform dictionary-based methods and the existing ontology annotation codebook and improve interpretability and efficiency. |
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference (C18-1)
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| Challenge: | Existing approaches to perform natural language inference ignore syntactic dependency among words or use tree-LSTM to generate sentence representation with irrelevant information. |
| Approach: | They propose to perform natural language inference with Word-Pair-Dependency-Triplets . they propose to compare the triplets of a given passage-pair to make judgement more interpretable . |
| Outcome: | The proposed approach is better than most of the approaches that use tree structures and comparable to other state-of-the-art approaches. |
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference (2022.tacl-1)
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| Challenge: | Existing methods for ultra-fine entity typing fail to capture type semantics because of the large number of types and the scarcity of data per type. |
| Approach: | They propose a method that formulates entity typing as a natural language inference problem . they use indirect supervision from NLI to infer type information as textual hypotheses . |
| Outcome: | The proposed method achieves state-of-the-art performance on the ultra-fine entity typing task with limited training data. |
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (N19-1)
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| Challenge: | Sentiment analysis (SA) is a computational task that aims to identify opinion polarity towards a specific aspect. |
| Approach: | They propose to convert ABSA into a sentence-pair classification task such as question answering and natural language inference. |
| Outcome: | The proposed model is fine-tuned and achieves state-of-the-art on SentiHood and SemEval-2014 datasets. |
Enhancing Descriptive Image Captioning with Natural Language Inference (2021.acl-short)
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| Challenge: | Existing captioning models tend to generate generic captions, but generating descriptive captions is important. |
| Approach: | They propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference. |
| Outcome: | The proposed method outperforms baseline models on MSCOCO metrics on descriptiveness and descriptiveness. |
Sentence Representations via Gaussian Embedding (2024.eacl-short)
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| Challenge: | Sentence embeddings represent a sentence's meaning as a point in a vector space and primarily use symmetric measures such as the cosine similarity to measure the similarity between sentences, they cannot capture asymmetric relationships between two sentences, such as entailment and hierarchical relations. |
| Approach: | They propose a Gaussian-distribution-based contrastive learning framework for sentence embedding that can handle asymmetric inter-sentential relations and a similarity measure for identifying entailment relations. |
| Outcome: | The proposed framework performs comparable to that of previous methods on natural language inference tasks and estimates direction of entailment relations, which is difficult with point representations. |
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 . |
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization (2023.findings-eacl)
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Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown
| Challenge: | Existing approaches to dialogue summarization rely on features of conversation data. |
| Approach: | They propose to use natural language inference models to improve coverage and faithfulness . they use fine-grained training signals to encourage model to generate missing content . |
| Outcome: | The proposed model achieves higher faithfulness and coverage while maintaining conciseness compared to prior methods. |
Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models (2023.emnlp-main)
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| Challenge: | Generalized quantifiers are used to indicate the proportions predicates satisfy (e.g., some apples are red). |
| Approach: | They propose a framework to model quantifier semantics for textbased foundation models by combining natural language inference and the Rational Speech Acts framework. |
| Outcome: | The proposed framework shows a 20% improvement over a literal listener baseline in predicting percentage scopes for quantifier comprehension even with no training. |
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models (2022.emnlp-main)
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| Challenge: | Traditional adversarial examples involve finding a small perturbation that does not change an input’s true label but confuses the classifier into outputting a different prediction. |
| Approach: | They propose to use contrastive learning to increase model robustness against fickle adversarial examples by reducing the vulnerability of adversarials to fickle ones. |
| Outcome: | The proposed method improves model robustness against fickle and obstinate adversarial examples. |
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020.tacl-1)
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| Challenge: | Recent work shows that pre-trained language models perform poorly on challenging datasets where spurious correlations do not hold. |
| Approach: | They propose to use multi-task learning to improve generalization from minority examples . they propose to combine MTL with auxiliary tasks to improve performance . |
| Outcome: | The proposed model generalizes from minority examples without hurting in-distribution performance. |
An Analysis under a Unified Formulation of Learning Algorithms with Output Constraints (2024.acl-srw)
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| Challenge: | Existing work on NN models with output constraints has not been able to categorize them in a unified manner. |
| Approach: | They propose new algorithms to integrate the information of main task and constraint injection . they use the H-score as a metric for considering main task metric and constrain infringement simultaneously . |
| Outcome: | The proposed algorithms integrate the information of main task and constraint injection, inspired by continual-learning algorithms. |
Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting (2023.emnlp-main)
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| Challenge: | Recent work shows how to prompt large language models with explanations to obtain strong performance on textual reasoning tasks. |
| Approach: | They propose to optimize explanation-infused prompts in a blackbox fashion by using leave-one-out schemes and a two-stage framework. |
| Outcome: | The proposed method improves prompts over crowdworker annotations and naive search strategies. |
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. |
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples (2024.acl-srw)
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| Challenge: | Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. |
| Approach: | They propose to automatically generate an NLI dataset with an LLM and use it for fine-tuning of PromptEOL. |
| Outcome: | The proposed model outperforms existing models on STS tasks without large manually annotated datasets. |
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization (2022.lrec-1)
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| Challenge: | In recent years, natural language inference has been an emerging research area . a new data augmentation technique is used to augment pre-trained language models . |
| Approach: | They propose to combine automatic contextual data augmentation with a learning procedure for natural language inference. |
| Outcome: | The proposed method outperforms baseline pre-trained language models on benchmark datasets and adversarial examples. |
COSY: COunterfactual SYntax for Cross-Lingual Understanding (2021.acl-long)
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| Challenge: | Pre-trained multilingual language models suffer from a large performance gap between source and target languages . e.g., multilingual-BERT models are widely used in cross-lingual tasks . |
| Approach: | They propose a language-agnostic approach to integrate universal syntax into language models . they use SYntax-aware networks and a COunterfactual training method . |
| Outcome: | The proposed model achieves state-of-the-art performance on natural language inference and question answering without auxiliary training data. |
How Fast can BERT Learn Simple Natural Language Inference? (2021.eacl-main)
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| Challenge: | Efficiency of learning of BERT is very slow due to hidden dataset bias . however, some studies show that it can learn with surface clues/patterns . |
| Approach: | They propose to use a simple entailment judgment case to test whether BERT can learn without hidden dataset bias. |
| Outcome: | The proposed case shows that BERT can learn without hidden bias without utilizing dataset bias. |
DefSent: Sentence Embeddings using Definition Sentences (2021.acl-short)
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| Challenge: | Sentence embedding methods using natural language inference datasets are limited for limited languages due to large datasets. |
| Approach: | They propose a sentence embedding method that uses definition sentences from a word dictionary. |
| Outcome: | The proposed method performs comparably on unsupervised semantics textual similarity tasks and slightly better on SentEval tasks than methods using large NLI datasets. |
Paraphrasing in Affirmative Terms Improves Negation Understanding (2024.acl-short)
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| Challenge: | Negation is a common linguistic phenomenon in human languages . however, language models face challenges with negation in many tasks . |
| Approach: | They propose to incorporate affirmative interpretations into models to make them more robust against negation. |
| Outcome: | The proposed models are more robust against negation when negation is present in input . the proposed models can be used to analyze large corpus and natural language understanding tasks . |
EconNLI: Evaluating Large Language Models on Economics Reasoning (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used for writing economic analysis reports or providing financial advice, but their ability to understand economic knowledge and reason about potential results of economic events or actions lacks systematic evaluation. |
| Approach: | They propose a dataset to evaluate LLMs’ knowledge and reasoning abilities in the economic domain. |
| Outcome: | The proposed dataset evaluates LLMs’ knowledge and reasoning abilities in the economic domain. |
Developmental Negation Processing in Transformer Language Models (2022.acl-short)
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| Challenge: | Negation is an important construct in language for reasoning over the truth of propositions, garnering interest from philosophy (Horn, 1989) and psycholinguistics (Zwaan, 2012). |
| Approach: | They propose to frame a natural language inference task as a problem and examine how well transformers can process negation categories. |
| Outcome: | The proposed models perform better on certain categories, suggesting clear differences in how they are processed. |
Compositional Zero-Shot Domain Transfer with Text-to-Text Models (2023.tacl-1)
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Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland
| Challenge: | Existing approaches to zero-shot domain transfer are limited by domain gap and lack of in-domain labels. |
| Approach: | They propose a compositional transfer learning framework (DoT51) that learns domain knowledge and task knowledge in a multi-task manner without access to in-domain labels. |
| Outcome: | The proposed framework outperforms the current state-of-the-art in zero-shot domain transfer by over 7 absolute points in accuracy on RadNLI. |
Rethinking STS and NLI in Large Language Models (2024.findings-eacl)
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| Challenge: | Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks. |
| Approach: | They propose to rethink semantic textual similarity (STS) and natural language inference (NLI) models with task-specific prompts and model overconfidence to capture disagreements between human judgements. |
| Outcome: | The proposed models are able to capture human opinions on individual examples without any parameter modifications. |
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation (2022.aacl-main)
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| Challenge: | Negation is an important linguistic phenomenon which denotes non-existence, denial, or contradiction. |
| Approach: | They propose a natural language inference test suite to test models for negation . they use a linguistic framework to analyze negation types and constructions . |
| Outcome: | The proposed test suite is more challenging than existing benchmarks on negation . it includes annotation of negation types and constructions grounded in linguistic theory . |
EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI (2026.findings-acl)
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| Challenge: | Human label variation (HLV) arises when multiple labels are valid for the same instance. |
| Approach: | They propose a framework for generating and validating explanations to detect errors using large language models (LLMs) EVADE framework provides broader explanation coverage and requires less human intervention . |
| Outcome: | The proposed framework provides broader explanation coverage, requires less human intervention, and delivers better downstream performance in predicting label distributions. |
GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge (2025.findings-naacl)
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| Challenge: | Contextualized word embeddings are available for many languages, but their coverage is limited for low resourced languages. |
| Approach: | They propose a method that integrates multilingual graph knowledge into the embeddings to make them green. |
| Outcome: | The proposed method outperforms state-of-the-art embeddings on lexical similarity task while being parameter-free at inference time. |
Benchmarking Long-tail Generalization with Likelihood Splits (2023.findings-eacl)
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| Challenge: | Existing methods to test out of distribution generalization have their own strengths and weaknesses. |
| Approach: | They propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets. |
| Outcome: | The proposed approach can be customized to construct meaningful splits for a wide range of tasks. |
Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service (2025.naacl-industry)
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| Challenge: | Hallucination is a problem in large language models that produce incorrect output . authors propose a reliable and high-speed production system to detect and rectify hallucinations . |
| Approach: | They propose a high-speed production system that detects hallucinations in LLMs . they propose NER, natural language inference, span-based detection and a rewriting mechanism . |
| Outcome: | The proposed system detects a wide range of hallucinations in LLM responses. |
Using contradictions improves question answering systems (2023.acl-short)
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| Challenge: | Existing systems that use contradiction to determine if a question is supported by background contexts do better than those that use entailment. |
| Approach: | They propose a method that incorporates contradiction in natural language inference (NLI) they propose to reformulate answers from QA systems as hypotheses and then select the best one based on the results. |
| Outcome: | The proposed method improves on multiple choice and extractive QA in two settings. |
Zero-Shot Text Classification with Self-Training (2022.emnlp-main)
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| Challenge: | Recent advances in large pretrained language models have increased attention to zero-shot text classification. |
| Approach: | They propose a plug-and-play method to bridge this gap by requiring only class names along with an unlabeled dataset. |
| Outcome: | The proposed model can be trained on a natural language inference dataset and performs on dozens of unseen tasks without the need for domain expertise or trial and error. |
Investigating Reasons for Disagreement in Natural Language Inference (2022.tacl-1)
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| Challenge: | Several disagreements in natural language inference (NLI) annotation are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts. |
| Approach: | They propose a 4-way classification approach and a multilabel classification approach for detecting disagreements in natural language inference annotations. |
| Outcome: | The proposed model is more expressive and gives better recall of possible interpretations in the data. |
Exploring Transitivity in Neural NLI Models through Veridicality (2021.eacl-main)
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| Challenge: | Despite recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities remains unclear. |
| Approach: | They propose an analysis method to evaluate whether models can draw inferences composed of veridical inference and arbitrary inference types. |
| Outcome: | The proposed model performs poorly on transitivity inference tasks, suggesting it lacks generalization capacity for drawing composite inferences from training examples. |
Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs (2025.coling-main)
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Tamzeed Mahfuz, Satak Kumar Dey, Ruwad Naswan, Hasnaen Adil, Khondker Salman Sayeed, Haz Sameen Shahgir
| Challenge: | a new generation of English-oriented Large Language Models significantly outperforms older LLMs on low-resource languages. |
| Approach: | They compare Bengali-oriented LLMs with open-weight and closed-source LLM models . they conclude that there is a need for a Bengali model, but lacks high-quality pretraining data . |
| Outcome: | The proposed model outperforms existing models on Bengali on low-resource languages . the results highlight biases in machine-translated datasets used for Bengali NLP tasks . |
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning (2022.findings-acl)
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| Challenge: | Using distributed NLI, we show that models can capture human judgement distribution more effectively than the softmax baseline. |
| Approach: | They propose a new NLU task to predict the distribution of human judgements . they propose Monte Carlo, Deep Ensemble, Re-Calibration and Distribution Distillation methods to capture human judgement distributions. |
| Outcome: | The proposed methods perform better than the softmax baseline, but the results are still far below the estimated human upper-bound. |
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference (N18-2)
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| Challenge: | Existing methods to investigate whether sentence representations from NMT systems capture distinct semantic phenomena are limited. |
| Approach: | They propose a process to investigate the extent to which sentence representations arising from neural machine translation systems encode distinct semantic phenomena. |
| Outcome: | The proposed model is suited to supporting inferences at the syntax-semantics interface, compared to anaphora resolution requiring world knowledge. |
Elastic weight consolidation for better bias inoculation (2021.eacl-main)
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| Challenge: | Recent studies have shown that the lack of suitable inductive biases in sentence-pair classification models can cause misclassifications on training datasets. |
| Approach: | They propose to use elastic weight consolidation (EWC) to fine-tune models to mitigate biases while being less susceptible to catastrophic forgetting. |
| Outcome: | The proposed model improves on fact verification and stress tests while maintaining the original task accuracy. |
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection (2020.findings-emnlp)
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| Challenge: | Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on NLP tasks. |
| Approach: | They propose a hybrid emoji-based Masked Language Model to leverage eojis across languages to improve the learning of short text messages. |
| Outcome: | The proposed model performs better on German, Italian and Spanish. |
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search (P18-1)
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| Challenge: | a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search is currently used to learn word representations. |
| Approach: | They propose a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search. |
| Outcome: | The proposed model is based on a large-scale lookup operation to ground language using image search. |
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Task-adaptive pre-training (TAPT) and Self-training can be complementary with simple TFS protocol. |
| Approach: | They propose to use task-adaptive pre-training and self-training to combine TAPT and ST with a simple TFS protocol to achieve strong combined gains across six datasets. |
| Outcome: | The proposed method can achieve strong combined gains across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. |
Differentiable Subset Pruning of Transformer Heads (2021.tacl-1)
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| Challenge: | Recent work shows that a large proportion of the heads in a Transformer’s multi-head attention mechanism can be safely pruned away without significantly harming the performance of the model. |
| Approach: | They propose a method that prunes a Transformer's multi-head attention mechanism away without significantly harming its performance. |
| Outcome: | The proposed method improves on natural language inference and machine translation tasks while offering precise control of sparsity level. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
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J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency (2021.findings-acl)
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| Challenge: | Existing methods to evaluate consistency capacity of open-domain chatbots are costly and low-efficient. |
| Approach: | They propose an efficient framework for evaluating consistency of open-domain chatbots . they use human judges to interact with chatbot, which is costly and low-efficient . |
| Outcome: | The proposed framework can assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation. |
Deep Generative Model for Joint Alignment and Word Representation (N18-1)
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
| Approach: | They exploit translation as a distributional context and embed words as posterior probability densities, rather than point estimates, which allows them to compare words in context using a measure of overlap between distributions. |
| Outcome: | The proposed model performs on a range of lexical semantics tasks and achieves competitive results on benchmarks including natural language inference, paraphrasing, and text similarity. |
Explainable Hallucination through Natural Language Inference Mapping (2025.findings-acl)
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| Challenge: | Large language models (LLMs) often generate hallucinated content, making it crucial to identify and quantify inconsistencies in their outputs. |
| Approach: | They propose a framework that maps entailment and contradiction relations between inputs and outputs using a natural language inference model. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by five percentage points while providing clear, interpretable explanations. |
Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)
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| Challenge: | despite the large amount of labeled datasets, there is an imbalance in data availability across languages. |
| Approach: | They explore cross-lingual knowledge transfer methods avoiding manual data curation . they use large multilingual encoders and translation systems, LLMs, and language adapters . |
| Outcome: | The proposed approaches are tested on three text classification tasks in Ukrainian . the authors show that the proposed approaches avoid manual data curation . |
Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks? (2021.findings-acl)
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| Challenge: | In July 2019, RoBERTa was the first to surpass a human baseline on GLUE . since then, 13 more methods have outperformed humans on the GLu leaderboard . |
| Approach: | They found that 75% to 90% of correct predictions of BERT-based classifiers remain constant after input words are randomly shuffled. |
| Outcome: | The proposed model outperforms humans on GLUE and SQuAD 2.0. |
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. |
Constructing a Japanese Verdict Prediction Dataset for Fact-Checking of LLM-Generated Texts (2026.acl-srw)
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Miwa Masano, Hirokazu Kiyomaru, Atsushi Keyaki, Kaito Horio, Rei Minamoto, Ribeka Keyaki, Kouta Nakayama, Hideyuki Tachibana, Daisuke Kawahara
| Challenge: | Text generated by Large Language Models (LLMs) may contain plausible but incorrect information known as hallucinations. |
| Approach: | They extend the label set for verdict prediction to capture claim-evidence relationships humans would commonly interpret as supported or refuted. |
| Outcome: | The proposed system improves F1 by 4 percentage points compared to baseline. |
Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)
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| Challenge: | Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI. |
| Approach: | They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input. |
| Outcome: | The proposed models perform better than models fine-tuned for BERT, but the results are in general negative. |
DeFT-X: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer (2025.findings-emnlp)
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| Challenge: | Prior studies have explored multiple approaches to combine task knowledge from task-specific data in a (high-resource) source language with language knowledge from unlabeled text in 'low-resourced' target language. |
| Approach: | They propose a composable sparse fine-tuning approach that learns task-specific and language-specific sparsen masks to select a subset of the pretrained model's parameters. |
| Outcome: | The proposed approach performs at par or outperforms SFT and other prominent cross-lingual transfer baselines. |
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (N18-1)
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| Challenge: | et al., 1996, show that many of the most actively studied problems in NLP depend in large part on natural language understanding (NLU). |
| Approach: | They propose a dataset for machine learning that uses ten different genres of English to evaluate sentences for their meanings. |
| Outcome: | The multi-genre natural language inference corpus is one of the largest available for natural language understanding. |
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)
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| Challenge: | Semantic similarity modeling is central to many NLP problems such as question answering. |
| Approach: | They propose a pairwise word interaction model with syntactic structure priors to explore their effectiveness. |
| Outcome: | Extensive evaluations on eight benchmark datasets show that incorporating structural information improves over strong baselines. |
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (2022.emnlp-main)
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Eric Mitchell, Joseph Noh, Siyan Li, Will Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, Christopher Manning
| Challenge: | Existing frameworks for boosting consistency and accuracy of pre-trained NLP models without fine-tuning or re-training are lacking. |
| Approach: | They propose a framework for boosting the consistency and accuracy of pre-trained NLP models using pre-trainer natural language inference models without fine-tuning or re-training. |
| Outcome: | The proposed framework boosts consistency and accuracy of pre-trained NLP models using pre-train natural language inference models without fine-tuning or re-training. |
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods of logical reasoning focus on entity-aware information but ignore hierarchical relations that may even have mutual effects. |
| Approach: | They propose a holistic graph network that deals with context at both discourse-level and word-level as the basis for logical reasoning. |
| Outcome: | The proposed method improves on logical reasoning QA datasets and natural language inference datasets. |
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation (2024.findings-acl)
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| Challenge: | Knowledge distillation optimises a smaller student model to behave similarly to a larger teacher model, retaining some performance benefits. |
| Approach: | They propose to augment the distillation with generated unlabelled examples that match the target distribution and upsamples data points among the training set that are similar to the target. |
| Outcome: | The proposed method outperforms previous robustness solutions on the task of natural language inference (NLI) it also improves performance on OOD domains even beyond the target domain. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
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Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |
NormNet: Normalize Noun Phrases for More Robust NLP (2023.findings-acl)
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| Challenge: | Existing approaches to debunk false features in deep NLP models are inadequate . previous work suggests that models learn spurious features instead of the true signals of the task . |
| Approach: | They propose a normalization strategy to eliminate false features caused by the textual surfaces of noun phrases. |
| Outcome: | The proposed normalization strategy eliminates false features caused by the textual surfaces of noun phrases. |
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models (2024.findings-acl)
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Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurelie Lozano, Payel Das, Georgios Kollias
| Challenge: | Transformer-based Language Models have become ubiquitous in natural language processing due to impressive performance on various tasks. |
| Approach: | They explore how sparsity affects network topology by exploiting mechanisms seen in biological networks . they show that model-agnostic sparsities are performant across diverse NLP tasks . |
| Outcome: | The proposed model-agnostic sparsity approaches are performant and efficient across NLP tasks. |
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities (2023.eacl-main)
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| Challenge: | Several proposals have been put forward for improving out-of-distribution performance by mitigating dataset biases. |
| Approach: | They propose a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts (PoE) loss function to further improve OOD performance. |
| Outcome: | The proposed method improves OOD performance while maintaining in-distribution performance. |
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. (2022.coling-1)
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| Challenge: | Reasoning and knowledge-related skills are considered as fundamental skills for natural language understanding (NLU) tasks. |
| Approach: | They propose a method to diagnose correlations between an NLU dataset and a specific skill. |
| Outcome: | The proposed method is able to diagnose correlations between dataset and logical reasoning skill on 8 MRC and 3 NLI datasets. |
Enhancing Sentence Embedding with Generalized Pooling (C18-1)
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| Challenge: | Existing methods for learning sentence embedding are limited, but still need to be improved. |
| Approach: | They propose a vector-based multi-head attention model that uses special cases of max pooling, mean pooling and scalar self-attention. |
| Outcome: | The proposed model improves on natural language inference, author profiling, and sentiment classification tasks. |
Combining Event Semantics and Degree Semantics for Natural Language Inference (2020.coling-main)
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| Challenge: | formal semantics has been used to account for the semantics of verb phrases and adverbial modifiers . but it is not clear whether these independent theories can be combined and extended to cases in which the phenomena in question interact. |
| Approach: | They propose a logic-based NLI system that combines event semantics and degree semantics. |
| Outcome: | The proposed system achieves high accuracies on linguistically challenging datasets . the proposed system can handle various combinations of linguistic phenomena without compromise . |
A Neural-Symbolic Approach to Natural Language Understanding (2022.findings-emnlp)
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| Challenge: | Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed. |
| Approach: | They propose a framework for NLU based on analogical reasoning based upon neural processing and logical reasoning using both neural and symbolic processing. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two NLU tasks, question answering (QA) and natural language inference (NLI). |
Investigating Multi-source Active Learning for Natural Language Inference (2023.eacl-main)
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| Challenge: | Recent studies often assume that training and test data are drawn from the same distribution. |
| Approach: | They propose to apply active learning to unlabelled data pools to test for learning and generalisation. |
| Outcome: | The proposed strategies outperform random selection and outperformed hard-to-learn data on the task of natural language inference. |
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information (2021.acl-long)
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| Challenge: | ChineseBERT model incorporates glyph and pinyin information of Chinese characters into pretraining . proposed model achieves new performance boost over baseline models with fewer training steps . |
| Approach: | They propose a ChineseBERT model that incorporates glyph and pinyin information into pretraining . the glyph embedding is obtained based on different fonts of a character, and the pinyink embeddment characterizes the pronunciation of Chinese characters. |
| Outcome: | The proposed model achieves new performance boosts over baseline models with fewer training steps. |
Prompting for explanations improves Adversarial NLI. Is this true? {Yes} it is {true} because {it weakens superficial cues} (2023.findings-eacl)
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| Challenge: | Explanation prompts are used to generate an explanation for a given input . they are also used to improve model performance on adversarial benchmarks . |
| Approach: | They propose to use explanation prompts to generate an explanation that supports a label . they argue that prompting for explanations weakens superficial cues . |
| Outcome: | The proposed explanation prompts outperform label-only prompts on adversarial benchmarks. |
UDAPTER - Efficient Domain Adaptation Using Adapters (2023.eacl-main)
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| Challenge: | Using adapters, unsupervised domain adaptation (UDA) is more parameter efficient and requires large-scale data to be effective. |
| Approach: | They propose to add small bottleneck layers to each layer of a pre-trained language model to make it more parameter efficient by adding adapters. |
| Outcome: | The proposed methods outperform unsupervised domain adaptation methods such as DANN and DSN in natural language inference and sentiment classification tasks. |
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction (2025.naacl-long)
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| Challenge: | Recent research has explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE). |
| Approach: | They propose a method that verbalizes relation classes into class-indicative hypotheses to align a traditionally multi-class classification task to one of textual entailment. |
| Outcome: | The proposed method improves relation extraction performance on BioRED and ReTACRED. |
Do Prompt-Based Models Really Understand the Meaning of Their Prompts? (2022.naacl-main)
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| Challenge: | Recent studies show that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. |
| Approach: | They experiment with 30 prompts manually written for natural language inference (NLI) they find that models can learn just as fast with many irrelevant or pathologically misleading prompts . |
| Outcome: | The proposed model can learn as fast with irrelevant or pathologically misleading prompts as with instructively “good” prompts. |
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling (2023.emnlp-main)
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| Challenge: | Recent studies suggest that transformer-based models perform cross-attention over input pairs, leading to computational cost. |
| Approach: | They propose a lightweight cross-attention mechanism that performs query encoding only once while modeling the query-candidate interaction in parallel. |
| Outcome: | The proposed model speeds up sentence pairing by over 113x while achieving comparable performance as the more expensive models. |
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer (2022.acl-long)
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| Challenge: | Existing sparse attention methods use fixed patterns to select words without considering similarities between words. |
| Approach: | They propose a neural clustering method which integrates into the Self-Attention Mechanism in Transformer and integrates it into the target task. |
| Outcome: | The proposed method outperforms two typical sparse attention methods on translation, text classification, and text matching tasks while having a comparable or even better time and memory efficiency. |
For Generated Text, Is NLI-Neutral Text the Best Text? (2023.findings-emnlp)
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| Challenge: | a perfectly informative agent would eschew utterances that are redundant or contradict that which they have already said. |
| Approach: | They propose to use a pre-trained NLI model to assess whether a sentence entails, contradicts, or is neutral to prompt and preceding text. |
| Outcome: | The proposed model is predictive of generation errors made by GPT-3. |
Probing Linguistic Systematicity (2020.acl-main)
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| Challenge: | Existing evidence that deep natural language understanding models do not learn systematically is lacking. |
| Approach: | They examine whether deep natural language understanding models exhibit systematicity . they find that network architectures can generalize non-systematically . |
| Outcome: | The proposed model generalizes non-systematically, but is unsatisfactory, the authors argue . they show that the current state-of-the-art models do not generalize systematically . |
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation (2023.acl-long)
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| Challenge: | Existing techniques to fine-tune pre-trained language models on downstream tasks are inadequate. |
| Approach: | They propose a technique to perturb hidden Transformers representations by enhancing generalization of hidden representations from different layers. |
| Outcome: | The proposed technique outperforms vanilla fine-tuning and enhances generalization of hidden representations from different layers. |
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media (2025.findings-naacl)
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Owen Cook, Charlie Grimshaw, Ben Peng Wu, Sophie Dillon, Jack Hicks, Luke Jones, Thomas Smith, Matyas Szert, Xingyi Song
| Challenge: | Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people. |
| Approach: | They propose to use inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability. |
| Outcome: | The proposed framework utilises inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability. |
Convolutional Interaction Network for Natural Language Inference (D18-1)
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| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
Penguins Don’t Fly: Reasoning about Generics through Instantiations and Exceptions (2023.eacl-main)
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| Challenge: | Generics express generalizations about the world that are not universally true . commonsense knowledge bases encode some generic knowledge but rarely enumerate exceptions . |
| Approach: | They propose a framework informed by linguistic theory to generate exemplars for generics . they generate 19k exemplar cases for 650 generics and show they outperform a strong baseline . |
| Outcome: | The proposed framework outperforms a baseline framework by 12.8 precision points. |
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown a great impact on NLP tasks. |
| Approach: | They propose an answer space clustered prompting model and a synonym initialization method that automatically categorizes all answer tokens in a semantic-clustered embedding space. |
| Outcome: | Experiments show that the proposed method outperforms existing state-of-the-art methods in few-shot settings. |
Conformal Predictor for Improving Zero-Shot Text Classification Efficiency (2022.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. |
| Approach: | They propose to limit the number of likely labels using a fast base classifier-based conformal predictor calibrated on samples labeled by the 0shot model. |
| Outcome: | The proposed models reduce the average inference time for NLI- and NSP-based models by 25.6% and 22.2% without dropping performance below the predefined error rate of 1%. |
Empowering Language Understanding with Counterfactual Reasoning (2021.findings-acl)
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| Challenge: | Existing methods for language understanding use the recognized patterns in the testing phase that are inherently different from us humans who have counterfactual thinking. |
| Approach: | They propose a counterfactual Reasoning Model which mimics counterfactive thinking by learning from few counterffact samples. |
| Outcome: | The proposed model can detect and make predictions from textual patterns . it can also detect negative sarcastic puns by comparing them with imaginations . |
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization (2022.naacl-main)
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| Challenge: | Neural abstractive summarization models generate factually inconsistent summaries . previous work has introduced the task of recognizing factual inconsistency as a downstream application of natural language inference (NLI). |
| Approach: | They propose a data generation pipeline that enables a task-oriented approach to detect factual inconsistencies in abstractive summarization models. |
| Outcome: | The proposed model improves the state-of-the-art performance across four benchmarks for recognizing factual inconsistency in generated summaries. |
MetaICL: Learning to Learn In Context (2022.naacl-main)
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| Challenge: | Large language models can do in-context learning by conditioning on a few training examples with no parameter updates or task-specific templates. |
| Approach: | They propose a meta-training framework where a pretrained language model is tuned to do in-context learning on a large set of training tasks. |
| Outcome: | The proposed framework outperforms baseline models on 142 NLP datasets and a range of target tasks with domain shifts. |
Service-oriented Text-to-SQL Parsing (2020.findings-emnlp)
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| Challenge: | TEXT2SQL models focus on generating complex SQL query in a precise and complete fashion . current models focus only on executing complex queries in production environment . TExT2sql models are limited by human labor and limited by predefined template . |
| Approach: | They propose a text-to-sql parser that translates natural language utterance to SQL query . the framework enables flexibly access database than rigid API in the application . |
| Outcome: | The proposed model outperforms baseline models in the WikiSQL task by 13% error reduction. |
BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP (2024.lrec-main)
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Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
| Challenge: | Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing. |
| Approach: | They propose to evaluate LLMs in Bengali to benchmark their performance . they select Bangla NLP tasks such as text summarization, question answering, paraphrasing . |
| Outcome: | The proposed model performs better in some tasks than current models, but in most tasks, it is poor . |
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals (2024.findings-emnlp)
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Yanai Elazar, Bhargavi Paranjape, Hao Peng, Sarah Wiegreffe, Khyathi Chandu, Vivek Srikumar, Sameer Singh, Noah Smith
| Challenge: | Existing studies have found that datasets with paired inputs are prone to spurious correlations, resulting in models trained only on those outperform chance. |
| Approach: | They propose a counterfactual attentiveness test to measure reliance on spurious correlations by replacing part of the input with its counterpart from a different example. |
| Outcome: | The proposed method improves models' attentiveness on ten datasets spanning four tasks: natural language inference, reading comprehension, paraphrase detection, and visual & language reasoning. |
Syntactic Data Augmentation Increases Robustness to Inference Heuristics (2020.acl-main)
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| Challenge: | Pretrained neural models lack sensitivity to word order on controlled challenge sets . augmentation methods that improve accuracy on standard training sets may be a problem . |
| Approach: | They propose to augment standard training sets with syntactically informative examples by applying syntastic transformations to sentences from the MNLI corpus. |
| Outcome: | The proposed method improved BERT’s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73 without affecting performance on the MNLI test set. |
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness (2022.findings-acl)
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| Challenge: | Data modification has been proposed as an effective solution for generalizing to out-of-domain (OOD) inputs. |
| Approach: | They propose to use data modification to generalize to out-of-domain inputs . they also analyze their adversarial robustness using a synthetic dataset . |
| Outcome: | The proposed data modification strategies improve OOD accuracy and AR, but data filtering hurts OOD on other tasks. |
Unsupervised Improvement of Factual Knowledge in Language Models (2023.eacl-main)
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| Challenge: | Masked language modeling (MLM) is often dominated by high-frequency words that are sub-optimal for learning factual knowledge. |
| Approach: | They propose an approach that forces the model to prioritize informative words in a fully unsupervised way. |
| Outcome: | The proposed approach significantly improves the performance of pretrained language models on factual recall, question answering, sentiment analysis, and natural language inference in a closed-book setting. |
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation (2022.findings-emnlp)
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| Challenge: | Prior work on counterfactual data augmentation only considered restricted classes of perturbations, limiting their effectiveness. |
| Approach: | They propose a retrieval-augmented framework for creating diverse counterfactual perturbations for CDA. |
| Outcome: | Experiments on natural language inference and sentiment analysis show that the proposed framework can be used to encourage diversity in manually authored perturbations. |
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers (2023.eacl-main)
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| Challenge: | Efforts to debias NLI have led to datasets that exhibit different kinds of bias than those shown before. |
| Approach: | They propose a new technique to detect and reduce single sentence label leakage . leakage is a problem with many modern NLI datasets, they argue . future work must prioritize reducing this problem, they write . |
| Outcome: | a new model-driven technique can detect leakage and detect subpopulations in the datasets which exhibit it . the proposed technique is based on the progressive evaluation of cluster outliers (PECO) . it allows objective measurement of leakage, and automatic detection of subpopulations in the data which exhibit leakage. |
Neural Natural Language Inference Models Enhanced with External Knowledge (P18-1)
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| Challenge: | Existing datasets that allow for complex models to be trained are limited . if data is not available, can machines learn all knowledge needed to perform natural language inference? |
| Approach: | They propose to enrich neural natural language inference models with external knowledge . they propose to use this knowledge to build NLI models to leverage it . |
| Outcome: | The proposed models improve on the SNLI and MultiNLI datasets. |
How well do NLI models capture verb veridicality? (D19-1)
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| Challenge: | In natural language inference, contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. |
| Approach: | They propose to use a dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. |
| Outcome: | The proposed model learns to make correct inferences about veridicality in verb-complement constructions. |
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? (2022.naacl-main)
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| Challenge: | Existing literature has focused on pretrainer-based text-driven brain encoding models . however, few studies have explored the efficacy of task-specific learning of Transformers . |
| Approach: | They propose to use ten popular natural language processing tasks to learn Transformer representations for predicting brain responses. |
| Outcome: | The proposed model predicts brain activity across the whole brain. |
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation (2024.findings-acl)
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| Challenge: | Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. |
| Approach: | They propose a Fact Extractor that leverages large language models to extract factual statements from radiology reports. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods in sentence ranking, natural language inference, and label extraction tasks. |
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
| Approach: | They propose to fine-tune pre-trained language models to optimize the zero-shot learning objective by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering format. |
| Outcome: | The proposed model outperforms a same-sized QA model and the previous SOTA zero-shot learning system on unseen tasks. |
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks (2025.findings-naacl)
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| Challenge: | Existing methods for uncertainty estimation are inadequate for safety-critical applications. |
| Approach: | They propose a method that uses the distances from neighbors and the ratio of labels in neighbors to estimate uncertainty. |
| Outcome: | The proposed method outperforms baseline and density-based methods in calibration and uncertainty metrics. |
Thunder-NUBench: A Benchmark for LLMs’ Sentence-Level Negation Understanding (2026.findings-eacl)
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| Challenge: | Negation is a fundamental linguistic phenomenon that poses ongoing challenges for Large Language Models (LLMs) Current benchmarks treat negation as a minor detail within broader tasks, such as natural language inference. |
| Approach: | They propose a novel benchmark specifically created to assess sentence-level understanding of negation in Large Language Models (LLMs). |
| Outcome: | The proposed benchmark compares standard negation with structurally diverse alternatives, such as local negation, contradiction, and paraphrase. |
Does Chinese BERT Encode Word Structure? (2020.coling-main)
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| Challenge: | Existing work has focused on analyzing the features captured by representative models such as BERT . however, little work has investigated word features for character languages such as Chinese . |
| Approach: | They investigate Chinese BERT using attention weight distribution statistics and probing tasks to understand word features. |
| Outcome: | The proposed model improves syntactic, semantic and word sense knowledge on a wide range of NLP tasks. |
Lifting the Curse of Multilinguality by Pre-training Modular Transformers (2022.naacl-main)
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| Challenge: | Recent work on multilingual pre-trained models has focused on pre-training transformers on concatenated corpora of a large number of languages. |
| Approach: | They propose a language-specific module approach that allows for more languages to be trained post-hoc. |
| Outcome: | The proposed model can be pre-trained on multiple languages with no drop in performance . |
Interpretation of NLP models through input marginalization (2020.emnlp-main)
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| Challenge: | Existing methods to interpret NLP predictions replace each token with a predefined value, resulting in misleading interpretations. |
| Approach: | They propose to marginalize each token out of the training data distribution to demystify the "black box" property of deep neural networks for natural language processing. |
| Outcome: | The proposed method marginalizes each token out of the training data distribution. |
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding (2022.lrec-1)
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| Challenge: | a number of studies have suggested that models induce universal text representations . current benchmarks focus on semantic phenomena, so pragmatics needs to be the focus . |
| Approach: | They propose a benchmark that unites 11 pragmatics-focused evaluation datasets for English. |
| Outcome: | The proposed benchmark shows that natural language inference does not result in genuinely universal representations. |
Identifying the limits of transformers when performing model-checking with natural language (2023.eacl-main)
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| Challenge: | Recent studies have focused on transformer models’ ability to perform reasoning on text, but the above question has not been adequately answered. |
| Approach: | They investigated the problem of model-checking with natural language to determine whether transformers can comprehend logical semantics in natural language. |
| Outcome: | The proposed model-checking problem is suited to address this issue but is untouched in natural language inference research. |
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (2021.findings-emnlp)
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| Challenge: | Existing models lack the reasoning abilities needed to find complex counterevidence. |
| Approach: | They propose a natural language inference model that finds counterevidence from diverse sources on the Web. |
| Outcome: | The proposed model outperforms baseline models for NLI tasks and finds complex counterevidence better. |
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching (D19-1)
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| Challenge: | Sentence matching is a key issue in natural language inference and paraphrase identification. |
| Approach: | They propose a semantics-oriented attention and deep fusion network (OSOA-DFN) that is oriented to the original semantic representation of another sentence and propagates attention information at each matching layer. |
| Outcome: | The proposed model can model sentence matching more precisely on three sentence matching benchmark datasets. |
How Does Selective Mechanism Improve Self-Attention Networks? (2020.acl-main)
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| Challenge: | Experimental results show that selective SANs outperform the standard SAN by paying more attention to content words that contribute to the meaning of the sentence. |
| Approach: | They propose to implement selective SANs with a flexible Gumbel-Softmax to improve word order encoding and structure modeling. |
| Outcome: | The proposed system outperforms the standard SANs on several representative NLP tasks including natural language inference, semantic role labelling, and machine translation. |
SentEval: An Evaluation Toolkit for Universal Sentence Representations (L18-1)
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| Challenge: | a toolkit for evaluating the quality of universal sentence representations is available for download and preprocessing . word embeddings are not trained to perform well on one specific task, but their value lies in their transferability . evaluation of general-purpose word and sentence embeddables has been problematic . |
| Approach: | They propose a toolkit to evaluate the quality of universal sentence representations. |
| Outcome: | The proposed toolkit includes scripts to download and preprocess datasets and an easy interface to evaluate sentence encoders. |
Can Large Language Models Capture Dissenting Human Voices? (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. |
| Approach: | They evaluate the performance and alignment of large language models with humans using Monte Carlo Estimation and Log Probability Estimationic methods to estimate the multinomial distribution. |
| Outcome: | The proposed models fail to capture human disagreement distribution and inference and human alignment performance plunge even further on data samples with high disagreement levels raising concerns about their natural language understanding ability and representativeness to a larger human population. |
Towards Debiasing Translation Artifacts (2022.naacl-main)
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| Challenge: | Existing studies show translation artifacts in translations influence performance of cross-lingual tasks. |
| Approach: | They propose a method to reduce translation artifacts by extending an established bias-removal technique. |
| Outcome: | The proposed method reduces translationese at sentence and word level . it is the first study to debias translations on a natural language inference task . |
Fact Verification on Knowledge Graph via Programmatic Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for fact verification on knowledge graphs use implicit reasoning to predict entailment between claims and KG triples. |
| Approach: | They propose a framework that integrates large language models for fact verification on knowledge graphs. |
| Outcome: | The proposed framework outperforms existing methods on knowledge graphs with 86.82% accuracy. |
From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel in answering questions pertaining to commonsense reasoning and inference. |
| Approach: | They prompt LLMs to generate items in the style of a benchmark for commonsense reasoning . they find that LLM authors that answer COPA items are more successful . |
| Outcome: | The authors' responses to their own items and their own generated items are better than those of the original LLMs. |
DISCO: Distilling Counterfactuals with Large Language Models (2023.acl-long)
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| Challenge: | high-quality counterfactual data is scarce for most tasks and not easily generated at scale. |
| Approach: | They propose a method for automatically generating high-quality counterfactual data at scale . they use a large general language model to generate phrasal perturbations and filter them . |
| Outcome: | The proposed method is task-agnostic and can be applied to the task of natural language inference. |
FlauBERT: Unsupervised Language Model Pre-training for French (2020.lrec-1)
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Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, Didier Schwab
| Challenge: | Language models are a key step to achieve state-of-the-art results in many different Natural Language Processing (NLP) tasks. |
| Approach: | They propose to use a language model that is pre-trained on a large and heterogeneous French corpus to train continuous word representations. |
| Outcome: | The proposed model outperforms existing models on a large and heterogeneous French corpus. |
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks (2021.naacl-main)
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| Challenge: | Existing methods to explain neural network models are computationally inefficient for text inputs. |
| Approach: | They propose a method to implicitly detect word correlations by grouping correlated words from input text pairs together and measuring their contribution to corresponding NLP tasks. |
| Outcome: | The proposed method is evaluated with two different model architectures across four datasets. |
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal (2024.findings-acl)
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| Challenge: | Abstract: Large language models are gaining widespread adoption in natural language processing tasks. |
| Approach: | They propose a semi-supervised approach to optimize for plausibility of extracted rationales by using a pre-trained natural language inference model and a supervised NLI predictor. |
| Outcome: | The proposed model outperforms unsupervised models by > 100% on a ERASER dataset. |
Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)
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| Challenge: | Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem. |
| Approach: | They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches . |
| Outcome: | The proposed methods highlight promising signals and challenges. |
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models (2023.findings-emnlp)
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| Challenge: | Recent studies demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. |
| Approach: | They propose to leverage explanations for small LMs to improve few-shot self-rationalization by reducing the problem of plausibility judgement to natural language inference. |
| Outcome: | The proposed approach achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. |
Mining Knowledge for Natural Language Inference from Wikipedia Categories (2020.findings-emnlp)
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| Challenge: | Accurate lexical entailment (LE) and natural language inference (NLI) tasks require expensive annotations. |
| Approach: | They propose to pretrain Wikipedia categories for lexical entailment and natural language inference by pretraining them on WikiNLI and transferring them to other knowledge bases. |
| Outcome: | The proposed model can improve strong baselines such as BERT and RoBERTa by pretraining on WikiNLI and transferring the models on downstream tasks. |
OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
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| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
| Approach: | They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations. |
| Outcome: | The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics. |
Lexical Generalization Improves with Larger Models and Longer Training (2022.findings-emnlp)
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| Challenge: | Excessive utilization of lexical overlap heuristics can lead to failure on challenging inputs. |
| Approach: | They analyze the use of lexical overlap heuristics in natural language inference, paraphrase detection, and reading comprehension using a contrastive dataset. |
| Outcome: | The proposed model can be used to improve performance on a wide range of tasks, but it is often wrong. |
Can NLI Models Verify QA Systems’ Predictions? (2021.findings-emnlp)
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| Challenge: | Recent question answering systems perform well on benchmark datasets, but are not always well-calibrated to spot spurious answers under distribution shifts. |
| Approach: | They propose to use natural language inference to verify whether answers are correct . they leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules. |
| Outcome: | The proposed approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
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| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (P19-1)
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| Challenge: | lexical overlap heuristics are effective for frequent example types but break down in more challenging cases. |
| Approach: | They propose to use a set of examples to test whether a sentence entails another . they propose to adopt three fallible syntactic heuristics for statistical NLI models . |
| Outcome: | The proposed model performs poorly on the HANS dataset, suggesting they have adopted these heuristics. |
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
| Approach: | They propose an automated annotation method that integrates an LLM and a natural language inference module to generate relation triples. |
| Outcome: | The proposed method can extract relations from document-level relation datasets with minimal human effort. |
ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference (2022.coling-1)
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| Challenge: | a large-scale corpus is needed for studies on natural language inference (NLI) for Vietnamese, which can be considered a low-resource language. |
| Approach: | They propose a corpus for evaluating Vietnamese natural language inference models . they use a human-annotated corpus extracted from more than 800 online news articles . |
| Outcome: | The ViNLI corpus is created and evaluated with a strict process of quality control . the best system performance is still far from human performance (a 14.20% gap in accuracy). |
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks (2021.findings-emnlp)
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| Challenge: | Recent studies have focused on rule-based and neural sequence-to-sequence (seq2sequ) TS is a technique that reduces text complexity for human consumption. |
| Approach: | They evaluate two possible uses of neural TS: simplifying input texts at prediction time and augmenting training data to provide machines with additional information during training. |
| Outcome: | The proposed approach improves performance on two datasets. |
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)
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| Challenge: | Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process. |
| Approach: | They propose a method to create prompts for a diverse set of tasks using a gradient-guided search. |
| Outcome: | The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
Analyzing Stereotypes in Generative Text Inference Tasks (2021.findings-acl)
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| Challenge: | Social psychology studies how social stereotypes are shared as part of cultural knowledge . |
| Approach: | They study how stereotypes manifest when potential targets are situated in neutral contexts . they collect human judgments on the presence of stereotypes in generated inferences based on annotator positionality . |
| Outcome: | The results show that the annotators' positions differ depending on the type of inferences they generate . |
KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction (2023.emnlp-main)
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| Challenge: | Existing work on hypernym-hyponym (“is-a”) relations is mostly in the English language. |
| Approach: | They propose a Knowledge Enhanced Prompt Learning method for Chinese hypernym-hyponym relation extraction using Hearst-like patterns as the prior knowledge. |
| Outcome: | The proposed method is able to extract hypernym-hyponym relations from Chinese unstructured texts using Hearst-like patterns and embed patterns and text simultaneously. |
Task Calibration: Calibrating Large Language Models on Inference Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive zero-shot performance on inference tasks, however, they may suffer from spurious correlations between input texts and output labels, which limits their ability to reason based purely on general language understanding. |
| Approach: | They propose a zero-shot and inference-only calibration method inspired by mutual information which recovers LLM performance through task reformulation. |
| Outcome: | The proposed calibration method improves on 13 benchmarks and prompt templates and can be integrated with other calibration methods. |
Dialogue Natural Language Inference (P19-1)
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| Challenge: | Consistency is a long standing issue faced by dialogue models. |
| Approach: | They propose to frame the consistency of dialogue agents as natural language inference and create a new natural language dataset called Dialogue NLI. |
| Outcome: | The proposed model can improve the consistency of a dialogue model with human evaluation and automatic metrics on a suite of evaluation sets designed to measure the model’s consistency. |
Temporal Reasoning in Natural Language Inference (2020.findings-emnlp)
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| Challenge: | We use five new natural language inference (NLI) datasets focused on temporal reasoning. |
| Approach: | They introduce five new natural language inference datasets focused on temporal reasoning. |
| Outcome: | The proposed models capture the temporal reasoning of four existing datasets. |
Happiness is Sharing a Vocabulary: A Study of Transliteration Methods (2026.eacl-long)
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| Challenge: | a key problem in multilingual NLP is script barrier, which makes it difficult to share knowledge between languages . a new study shows that transliteration can be useful for languages using non-Latin scripts . |
| Approach: | They propose to use romanization, phonemic transcription, and substitution ciphers to evaluate models . romanization outperforms other input types in 7 out of 8 evaluation settings . |
| Outcome: | The proposed approach outperforms other input types on three tasks and is the most effective . romanization outperformed other input type in 7 out of 8 evaluation settings . |
Recursive Routing Networks: Learning to Compose Modules for Language Understanding (N19-1)
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Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen
| Challenge: | Recursive Routing Networks are modular, adaptable models that learn effectively in diverse environments. |
| Approach: | They propose to apply Recursive Routing Networks (RRNs) to natural language understanding by integrating them into existing architectures and recurrent network hidden layers. |
| Outcome: | The proposed model optimizes the parameters of the functions and the meta-learner decision-making component for routing inputs through those functions. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
Zero-Shot Cross-Lingual Transfer with Meta Learning (2020.emnlp-main)
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| Challenge: | There are more than 7,000 languages spoken in the world, over 90 of which have more than 10 million native speakers each. |
| Approach: | They propose to use meta-learning to train a model on multiple languages at the same time . they use standard supervised, zero-shot cross-lingual, and few-shot crosses-lingual settings for different natural language understanding tasks. |
| Outcome: | The proposed setup improves on the state-of-the-art for a total of 15 languages. |
Marked Attribute Bias in Natural Language Inference (2021.findings-acl)
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| Challenge: | Existing tests for gender-biased word embeddings do not address marked attribute bias . authors propose a new type of intrinsic bias measure for static word embeds . |
| Approach: | They propose a method to detect gender-biased word embeddings in a downstream NLP application . they propose 'debiasing' method to measure the marked attribute bias in embeddable word embeds . |
| Outcome: | The proposed method achieves best results on the marked attribute bias test set. |
Scalar Adjective Identification and Multilingual Ranking (2021.naacl-main)
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| Challenge: | Existing studies on scalar adjective ranking have focused on English due to the availability of datasets for evaluation. |
| Approach: | They propose a binary classification task to examine the models’ ability to distinguish scalar from relational adjectives in English. |
| Outcome: | The proposed task compares the models' ability to distinguish scalar from relational adjectives in English using monolingual and multilingual models. |
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence (2021.findings-acl)
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| Challenge: | a new corpus-level evaluation approach for event extraction is needed in social science applications . human annotations are often required to extract the actions of political actors and actors . a novel corpus evaluation approach can guide creation of similar social science-oriented resources . |
| Approach: | They propose a corpus-based approach to event extraction that integrates corpus evaluation with real-world social science . they use human annotations to read and label every document for mentions of police activity events . |
| Outcome: | The proposed method can guide creation of similar social-science-oriented resources in the future. |
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity (2024.lrec-main)
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| Challenge: | Using labeled NLI datasets for learning sentence embeddings leads to improved performance for natural language understanding tasks. |
| Approach: | They compare two data augmentation techniques for learning better sentence embeddings . they use a cross-lingual transfer technique that exploits English resources as training data to yield non-English sentence embeds as zero-shot inference . |
| Outcome: | The proposed techniques yield better performance on Japanese and Korean sentences. |
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)
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| Challenge: | Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability but their large size results in high inference latency. |
| Approach: | They propose an unsupervised domain adaptation framework that employs knowledge distillation to achieve domain-invariant representations at each layer. |
| Outcome: | The proposed framework outperforms early exit methods and domain adaptation methods under domain shift scenarios. |
FLamE: Few-shot Learning from Natural Language Explanations (2023.acl-long)
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| Challenge: | Recent work has shown limited utility of natural language explanations in improving classification. |
| Approach: | They propose a two-stage few-shot learning framework that generates explanations and fine-tunes a smaller model with generated explanations. |
| Outcome: | The proposed framework increases inference accuracy over strong baselines, but human evaluation reveals that the majority of generated explanations does not adequately justify classification decisions. |
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder (2020.findings-emnlp)
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| Challenge: | Using a poison signature, attackers can manipulate training data to manipulate the target class at test time. |
| Approach: | They propose a backdoor poisoning attack that generates poisoned training samples by poison injection in latent space and a conditional adversarially regularized autoencoder to generate poisones. |
| Outcome: | The proposed attack generates poisoned training samples by poison injection in latent space and shows that the target class can be steered to the poison class with success rates of >80% when the input hypothesis is injected with the poison signature. |
Persian Natural Language Inference: A Meta-learning Approach (2022.coling-1)
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| Challenge: | In general, shared representations are learned separately, either across tasks or across languages. |
| Approach: | They propose a meta-learning approach for inferring natural language in Persian . they use different task information or other language information to form additional high-quality tasks . |
| Outcome: | The proposed method outperforms the baseline approach, improving accuracy by roughly six percent. |
Conditional Dichotomy Quantification via Geometric Embedding (2025.acl-long)
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| Challenge: | Existing methods that rely on semantic similarity fail to capture the nuanced oppositional dynamics essential for these applications. |
| Approach: | They propose a task that formalizes the measurement of conditional dichotomy by using a dichotomian framework. |
| Outcome: | The proposed framework provides carefully constructed datasets covering debate, defeasible inference, and causal reasoning scenarios. |
MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing (2025.findings-naacl)
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| Challenge: | Existing models fail to capture important semantic features of logic such as monotonicity and negation. |
| Approach: | They propose a modular step-by-step approach to natural language inference . they use a language model to generate edits to incrementally transform the premise into the hypothesis . |
| Outcome: | The proposed method outperforms baseline models in realistic cross-domain settings with improvements up to 12.6% (relative). |
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset (2022.findings-emnlp)
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| Challenge: | JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. |
| Approach: | They propose to use JamPatoisNLI to provide a dataset for natural language inference in a creole language, Jamaican Patois. |
| Outcome: | The proposed dataset shows that the language's relatedness to English significantly boosts the effectiveness of cross-lingual transfer even for the higher order task. |
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive abilities on various tasks. however, fundamentally improving them depends on high-quality datasets or computationally expensive fine-tuning. |
| Approach: | They propose a framework to let Large Language Models self-improve without annotated datasets or parameter updates. |
| Outcome: | The proposed framework can help ChatGPT significantly improve its abilities in arithmetic reasoning, commonsense reasoning, factual reasoning, and natural language inference. |
Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Negation poses a challenge in many natural language understanding tasks . leveraging sentences with negation and affirmative interpretations is beneficial for many tasks involving humans . |
| Approach: | They propose to collect negated sentences and their affirmative interpretations and leverage them to build a plug-and-play neural generator that generates an affirmative interpreter. |
| Outcome: | The proposed method does not require manual effort and does not impact other tasks. |
Reordering Examples Helps during Priming-based Few-Shot Learning (2021.findings-acl)
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| Challenge: | Existing methods for learning from limited data are not efficient . we show that presenting examples in the right order is key for generalization . |
| Approach: | They propose a method to learn from limited data using examples as prompts . they propose PERO, which uses examples as search over set of permutations . |
| Outcome: | The proposed method can generalize using as few as 10 examples, the authors show . it can be used on sentiment classification, natural language inference and fact retrieval tasks . |
Mitigating the Diminishing Effect of Elastic Weight Consolidation (2022.coling-1)
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| Challenge: | Existing work addresses catastrophic forgetting in sequential training by fine-tuning pre-trained language models on different datasets. |
| Approach: | They propose to rescale the components of EWC to mitigate catastrophic forgetting by mixing new and old training data and retraining the model from scratch. |
| Outcome: | The proposed method requires smaller values for the trade-off parameters to achieve comparable results to EWC on natural language inference and fact-checking tasks. |
A Logic-Driven Framework for Consistency of Neural Models (D19-1)
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| Challenge: | Recent advances in NLP have improved performance on benchmarks such as GLUE . however, tracking performance on a leaderboard is not sufficient to characterize model quality . |
| Approach: | They propose a framework for constraining neural models using logic rules to regularize them away from inconsistency. |
| Outcome: | The proposed framework can be used on natural language inference and is compatible with off-the-shelf learning schemes without model redesign. |
Phrase-level Self-Attention Networks for Universal Sentence Encoding (D18-1)
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| Challenge: | Phrase-level self-attention networks (PSAN) can capture context dependencies at the phrase level instead of the sentence level. |
| Approach: | They propose to perform self-attention across words inside a phrase to capture context dependencies at the phrase level and use the gated memory updating mechanism to refine each word’s representation hierarchically with longer-term context dependency captured in a larger phrase. |
| Outcome: | The proposed model can achieve state-of-the-art performance across a plethora of NLP tasks including binary and multi-class classification, natural language inference and sentence similarity. |
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference (2020.emnlp-main)
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Jianguo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip Yu, Richard Socher, Caiming Xiong
| Challenge: | Existing work on few-shot intent classification without OOS has focused on the few-shot intent classification with out-of-scope intents. |
| Approach: | They propose to use BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. |
| Outcome: | The proposed approach achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches. |
Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)
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| Challenge: | Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing . |
| Approach: | They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach. |
| Outcome: | Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering. |
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation (2021.naacl-main)
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| Challenge: | Existing report generation systems suffer from incomplete and inconsistent generation, despite achieving high performance on natural language metrics such as CIDEr and BLEU. |
| Approach: | They propose two new rewards that encourage the generation of factually complete and consistent radiology reports by using an existing semantic equivalence metric. |
| Outcome: | The proposed system significantly improves the F1 score of a clinical information extraction performance on two open radiology report datasets. |
Thinking Like a Skeptic: Defeasible Inference in Natural Language (2020.findings-emnlp)
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Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith, Yejin Choi
| Challenge: | Defeasible inference is a mode of reasoning in which an inference may be weakened or overturned in light of new evidence. |
| Approach: | They propose a dataset for defeasible inference in natural language that includes extensions to existing inference datasets. |
| Outcome: | Defeasible NLI extends existing datasets for defeaasibility inference in natural language . generative models can weaken or strengthen inferences up to 68% of the time, it shows . |
Does Putting a Linguist in the Loop Improve NLU Data Collection? (2021.findings-emnlp)
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Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman
| Challenge: | Many datasets for training and evaluating natural language understanding (NLU) models contain systematic artifacts that are identified only after data collection is complete. |
| Approach: | They propose to have linguists identify artifacts and gaps in the data and communicate with non-expert crowdworkers to adjust task instructions and incentives. |
| Outcome: | The proposed protocol does not increase accuracy on out-of-domain test sets, and adds a chatroom does not. |
Certified Robustness to Adversarial Word Substitutions (D19-1)
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| Challenge: | State-of-the-art NLP models can be fooled by adversaries that apply seemingly innocuous label-preserving transformations to input text. |
| Approach: | They propose to train models that are provably robust to all word substitutions in a family of label-preserving transformations that can be replaced with a similar word without changing the original sentiment. |
| Outcome: | The proposed models achieve 75% adversarial accuracy on both sentiment analysis and natural language inference on IMDB and SNLI compared to models trained normally and ones trained with data augmentation. |
Can Explanations Be Useful for Calibrating Black Box Models? (2022.acl-long)
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| Challenge: | Existing models are often used as black boxes to adapt to new domains, but there is no single recipe for making them work. |
| Approach: | They propose to use black box models to improve their performance on new domains by leveraging explanations of their behavior. |
| Outcome: | The proposed method improves model generalization performance on two tasks using explanations. |
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks (2025.coling-main)
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| Challenge: | Recent advances in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a benchmark for textual adversarial defence that evaluates state-of-the-art defence mechanisms across diverse datasets, models, and tasks. |
| Outcome: | The proposed benchmark incorporates a wide range of datasets and evaluates state-of-the-art defence mechanisms. |
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation (2025.findings-naacl)
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| Challenge: | Hallucination is a popular topic in natural language generation (NLG). |
| Approach: | They propose to use large language models to evaluate faithfulness of guided NLGs by a rubric template and large language inference models to score the generation on quantifiable scales. |
| Outcome: | The proposed system can provide accurate judgement and explain whether a source and generation are factually consistent. |
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds (D19-1)
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| Challenge: | Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. |
| Approach: | They propose to use IRT models generated from artificial crowds of DNNs to learn IRT. |
| Outcome: | The proposed model learning method outperforms baseline methods for two NLP tasks. |
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference (2022.naacl-main)
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| Challenge: | a method for learning an NLI model is time-consuming and resource-intensive, but it can save time and resources. |
| Approach: | They propose a method for predicting model performance without fine-tuning it . they compare sentence embeddings with cosine similarity to classifiers . |
| Outcome: | The proposed method can save time and resources by comparing pre-trained models to real-world datasets. |
Validity Assessment of Legal Will Statements as Natural Language Inference (2022.findings-emnlp)
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| Challenge: | This study introduces a dataset that focuses on the validity of statements in legal wills. |
| Approach: | They propose a dataset that focuses on the validity of statements in legal wills. |
| Outcome: | The proposed model achieves 80% macro F1 and accuracy, but group accuracy is in mid 80s at best, suggesting that the models’ understanding of the task remains superficial. |
Enhancing Generalization in Natural Language Inference by Syntax (2020.findings-emnlp)
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| Challenge: | Pre-trained language models such as BERT have the state-of-the-art performance on natural language inference (NLI). |
| Approach: | They propose to use dependency trees to enhance generalization of BERT in a natural language inference task by leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns. |
| Outcome: | The proposed method makes BERT more robust on syntactic changes. |
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks (2020.coling-main)
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| Challenge: | Pre-trained transformer models have shown great success in improving performance on downstream tasks, but fine-tuning on a new task still requires large amounts of labeled data. |
| Approach: | They propose a method which allows optimization-based meta-learning across tasks . they use transformers to train transformer models and find better generalizations . |
| Outcome: | The proposed method outperforms self-supervised training and pre-trained models on 17 NLP tasks. |
On Reference (In-)Determinacy in Natural Language Inference (2025.findings-naacl)
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Sihao Chen, Chaitanya Malaviya, Alex Fabrikant, Hagai Taitelbaum, Tal Schuster, Senaka Buthpitiya, Dan Roth
| Challenge: | Using reference determinacy, models fail to recognize context mismatch in NLI examples . |
| Approach: | They propose a benchmark to identify reference ambiguity in NLI examples . they propose RD as a possible assumption for natural language inference . |
| Outcome: | The proposed benchmark identifies reference ambiguity in natural language inference examples . 80% false contradiction and >50% entailment predictions are found . |
Posing Fair Generalization Tasks for Natural Language Inference (D19-1)
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| Challenge: | Existing evaluation methods for deep learning semantics rely on naturalistic corpora, but they often fail to support the kind of generalization we are asking for. |
| Approach: | They define and motivate a formal notion of fairness for evaluations of deep learning models for semantics . they then apply it to natural language inference by constructing challenging but provably fair artificial datasets based on the results . |
| Outcome: | The proposed evaluations show that standard neural models fail to generalize in the required ways and even these models do not solve the task perfectly. |
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining (2021.acl-long)
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| Challenge: | Existing knowledge-based PLMs are based on linked-entity information, but they only use linked-enemy information as auxiliary information. |
| Approach: | They propose to integrate semantic knowledge from neighbours of linked-entity into a medical PLM that integrates heterogeneous-entities into the homogeneously neighbouring entity structure. |
| Outcome: | Experiments show that SMedBERT outperforms baselines in knowledge-intensive Chinese medical tasks. |
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks (D18-1)
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| Challenge: | Existing gated recurrent networks have a vanishing gradient, allowing for more matrix transformations and less transparent functions. |
| Approach: | They propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. |
| Outcome: | The proposed system is more transparent than LSTM/GRU due to the simplification. |
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task (2022.lrec-1)
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| Challenge: | Recent research questions whether these models really understand the meaning of natural language. |
| Approach: | They propose to transform the disambiguation of causative-passive homonymy (CPH) to a challenging natural language inference task using a pretrained transformer model RoBERTa. |
| Outcome: | The pretrained model RoBERTa performs poorly on the CANLI dataset . the model's internal representation of CPH is not captured in the model . |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |
Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models (2025.naacl-long)
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| Challenge: | In the recent past, a popular way of evaluating natural language understanding was to consider a model’s ability to perform natural language inference (NLI) tasks. |
| Approach: | They focus on five different NLI benchmarks across six models of different scales and examine how their accuracies develop during training. |
| Outcome: | The softmax distributions of models align with human label distributions in cases where statements are ambiguous or vague. |
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)
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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai
| Challenge: | Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training. |
| Approach: | They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks. |
| Outcome: | The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning. |
Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)
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| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
| Approach: | They propose a framework that assesses LLM-generated text based on semantic understanding. |
| Outcome: | The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4. |
FLUTE: Figurative Language Understanding through Textual Explanations (2022.emnlp-main)
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| Challenge: | Figurative language understanding is a recognizing textual entailment task, but lacks data for figurative language. |
| Approach: | They propose to use a dataset to analyze figurative NLI instances with explanations to improve models' performance. |
| Outcome: | The proposed dataset can scale up models even for figurative language using human annotations. |
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation (2022.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Sentence representations are essential in many NLP tasks operating at the sentence level. |
| Approach: | They propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap for smaller models. |
| Outcome: | The proposed method outperforms supervised training on STS, text classification, and natural language inference tasks on smaller models. |
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark (2023.acl-long)
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Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Veselin Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev
| Challenge: | bgGLUE is a benchmark for evaluating language models on natural language understanding (NLU) tasks in Bulgarian. |
| Approach: | They propose to use a benchmark to evaluate language models on NLU tasks in Bulgarian. |
| Outcome: | The proposed model performs well on sequence labeling tasks, but there is room for improvement for tasks that require more complex reasoning. |
What does the Failure to Reason with “Respectively” in Zero/Few-Shot Settings Tell Us about Language Models? (2023.acl-long)
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| Challenge: | In the context of natural language inference, we examine how language models reason with respective readings from two perspectives: syntactic-semantic and commonsense-world knowledge. |
| Approach: | They propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResLI to encompass various explicit and implicit realizations of "respectively". |
| Outcome: | The proposed datasets include explicit and implicit readings of "respectively" the proposed dataset shows that fine-tuned models struggle with understanding readings without explicit supervision. |
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions (2020.acl-main)
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| Challenge: | Modern deep learning models for NLP are notoriously opaque, and this has motivated efforts to design example-specific approaches to interpret such models. |
| Approach: | They propose to use influence functions to explain models by highlighting important words in input text to provide models with an explanation. |
| Outcome: | The proposed approach is particularly useful for natural language inference, a task in which ‘saliency maps’ may not have clear interpretation. |
Characterizing and Measuring Linguistic Dataset Drift (2023.acl-long)
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Tyler Chang, Kishaloy Halder, Neha Anna John, Yogarshi Vyas, Yassine Benajiba, Miguel Ballesteros, Dan Roth
| Challenge: | Existing metrics for dataset drift have not considered specific dimensions of linguistic drift that affect model performance. |
| Approach: | They propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift. |
| Outcome: | The proposed metrics are more effective than previous metrics at predicting out-of-domain model accuracies compared to popular fine-tuned embedding distances . |
Universal Sentence Representation Learning with Conditional Masked Language Model (2021.emnlp-main)
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| Challenge: | Existing methods to learn sentence representations on unlabeled corpora are difficult and expensive to obtain, making it hard to cover many domains and languages. |
| Approach: | They propose a method to train sentence representations on large unlabeled corpora by conditioning on the encoded vectors of adjacent sentences. |
| Outcome: | The proposed method outperforms existing models on SentEval and can be extended to a broad range of languages and domains. |
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation (2022.findings-emnlp)
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| Challenge: | Existing datasets are often flooded with repetitive and spurious patterns, leading to a lack of linguistic diversity. |
| Approach: | They propose a method that uses cartography to automatically identify and filter examples that demonstrate challenging reasoning patterns and then automatically compose new ones with similar patterns. |
| Outcome: | The proposed approach improves performance on eight out-of-domain test sets compared to training on the 4x larger MultiNLI dataset. |
Culturally Aware Natural Language Inference (2023.findings-emnlp)
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| Challenge: | Cultural norms are behavioral rules and conventions shared within specific groups, connecting cultural symbols and values. |
| Approach: | They propose a task that operationalizes cultural variations in language understanding through a natural language inference task that surfaces cultural variations as label disagreement between annotators from different cultural groups. |
| Outcome: | The proposed model can be evaluated at which levels it is culturally aware. |
Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)
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Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim
| Challenge: | Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning. |
| Approach: | They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling. |
| Outcome: | The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios. |
Stubborn Lexical Bias in Data and Models (2023.findings-acl)
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| Challenge: | Recent work has focused on spurious correlations between features and labels in training data . but, we find strong evidence of corresponding bias in the trained models . |
| Approach: | They propose a method to reduce spurious correlations in training data by reweighting it using a large pool of extracted features. |
| Outcome: | The proposed method reduces spurious correlations in training data, but still finds strong evidence of bias in trained models. |
Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages (2023.findings-acl)
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| Challenge: | Multilingual pretrained language models (MPLMs) perform strongly in cross-lingual transfer. |
| Approach: | They propose to augment context with similar sentences retrieved from a high-resource language (HRL) they find a significant correlation between cross-lingual transfer performance and similarity between high- and low-resourced languages . |
| Outcome: | The proposed model outperforms finetuning by 3.7% on three downstream tasks with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled and labeled settings. |
Finding a Balanced Degree of Automation for Summary Evaluation (2021.emnlp-main)
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| Challenge: | Automated summarization metrics are reliable but often poorly correlated with human judgment. |
| Approach: | They propose a semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. |
| Outcome: | The proposed metrics are semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. |
Bridging Fairness and Environmental Sustainability in Natural Language Processing (2022.emnlp-main)
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| Challenge: | a lack of research on the interplay between fairness and environmental impact is a problem in natural language processing . fairness is prone to encode and amplify stereotypical social biases, according to several studies . |
| Approach: | They evaluate a technique to reduce energy consumption of English NLP models by knowledge distillation for its impact on fairness. |
| Outcome: | The proposed method reduces energy consumption and environmental impact of English NLP models. |
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
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| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
| Approach: | They propose to interpret the intermediate layers of deep models by visualizing the saliency of attention and LSTM gating signals. |
| Outcome: | The proposed methods reveal interesting insights and identify critical information contributing to the model decisions. |
Zero-Shot Entailment Learning for Ontology-Based Biomedical Annotation Without Explicit Mentions (2025.coling-main)
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Rumana Ferdous Munne, Noriki Nishida, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Automated biomedical annotation presents significant challenges when entities are not explicitly mentioned in the text. |
| Approach: | They propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. |
| Outcome: | The proposed method performs well when processes are not explicitly mentioned . it is time-consuming and expensive to annotate biomedical texts with a specific ontology . |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic (2023.acl-long)
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| Challenge: | Despite the recent advances in distributed representation and neural networks, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. |
| Approach: | They propose to use logic formalism to perform systematic attacks centring around natural logic to generate better adversarial examples with fewer visits to the victim models. |
| Outcome: | The proposed framework generates better adversarial examples with fewer visits to the victim models. |
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking (2024.findings-emnlp)
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| Challenge: | Multiple-choice cloze tests are a prevalent form of assessment that evaluates students' comprehension and inference abilities. |
| Approach: | They propose a framework for distractor generation using readily available pre-trained language models . human evaluations confirm that their approach produces more effective distractors . |
| Outcome: | The proposed framework outperforms existing methods without training or fine-tuning human evaluations confirm it. |
Less Mature is More Adaptable for Sentence-level Language Modeling (2025.acl-long)
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| Challenge: | Existing studies fine-tune encoders or contrastive learning approaches to learn sentence representations. |
| Approach: | They propose to use sentence-level models to study how sentence representations influence downstream task performance. |
| Outcome: | The proposed models outperform token-level models in terms of time and data efficiency. |
Natural Language Annotations for Reasoning about Program Semantics (2023.findings-emnlp)
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| Challenge: | Xu et al., 2022) and Tafjord eet . al. 2021) have shown that programming assistants can explain their work by grounding natural language inference in code. |
| Approach: | They propose a dataset and protocol for annotating programs with natural language predicates at a finer granularity than code comments without relying on internal compiler representations. |
| Outcome: | The proposed method can be used to ground natural language inference in code without static analysis and without internal compiler representations. |
Example-based Hypernetworks for Multi-source Adaptation to Unseen Domains (2023.findings-emnlp)
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| Challenge: | In order to achieve unprecedented performance, many out-of-distribution generalization approaches use unlabeled data from the target distribution. |
| Approach: | They propose a framework that leverages labeled data from multiple source domains to generalize to unknown target domains at training. |
| Outcome: | The proposed framework outperforms existing models in two tasks, and it is compared to few-shot GPT-3. |
Baselines and Test Data for Cross-Lingual Inference (L18-1)
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| Challenge: | Recent research on textual entailment is limited to English, but it is expanding to other languages. |
| Approach: | They propose to extend the research in SNLI-style natural language inference toward multilingual evaluation by using cross-lingual word embeddings and machine translation. |
| Outcome: | The proposed system scores an average accuracy of just over 75%, but it is not perfect. |
Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity. |
| Approach: | They propose a framework to augment ABSA data using pseudo labels for target domain . they refine generated labeled data using a natural language inference filter . |
| Outcome: | The proposed framework outperforms 7 strong baselines on 4 kinds of ABSA tasks. |
Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)
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Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li
| Challenge: | Large-scale generative language models such as GPT-3 are competitive few-shot learners. |
| Approach: | They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities. |
| Outcome: | The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions. |
Translation Artifacts in Cross-lingual Transfer Learning (2020.emnlp-main)
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| Challenge: | Existing cross-lingual transfer learning techniques involve human and machine translations. |
| Approach: | They propose to use machine translation to translate test set or training set to introduce subtle artifacts that have a notable impact in existing cross-lingual models. |
| Outcome: | The proposed translation process reduces the lexical overlap between the premise and hypothesis by 4.3 and 2.8 points . the proposed translation-test and zero-shot approaches improve on previous work . |
Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021.emnlp-main)
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| Challenge: | Existing evaluation methods for factual consistency in knowledge-grounded dialogues are unreliable and limit their applicability. |
| Approach: | They propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering. |
| Outcome: | The proposed evaluation metric consistently shows higher correlation with human judgements. |
Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching (D19-1)
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| Challenge: | Recent work on text sequence matching tasks uses task specific supervised datasets, which are always limited to the amount due to the cost of annotation. |
| Approach: | They propose an aggregation method to combine Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching. |
| Outcome: | The proposed model improves on two publicly available datasets, WikiQA and SNLI. |
AlignScore: Evaluating Factual Consistency with A Unified Alignment Function (2023.acl-long)
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| Challenge: | Existing methods to evaluate factual consistency of text depend on limited data . e.g., generated text can contain factual inconsistencies that are irrelevant to context . |
| Approach: | They propose a new holistic metric that measures factual inconsistencies . they use 4.7M training examples from 7 well-established tasks . |
| Outcome: | The proposed metric outperforms existing metrics on 22 datasets and matches or outperFORMs them. |
Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
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| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking (2025.coling-main)
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| Challenge: | Claim matching (CM) is a binary classification task that can be used to determine if two claims can be verified using the same piece of evidence or fact-check. |
| Approach: | They propose a claim matching task that uses binary classification and large language models to test out learning approaches to the task. |
| Outcome: | The proposed task can be tackled by leveraging mature tasks such as natural language inference or paraphrase detection. |
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)
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| Challenge: | Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate. |
| Approach: | They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom . |
| Outcome: | The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios. |
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference (2020.emnlp-main)
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| Challenge: | Recent work has shown advantages of generative classifiers in terms of data efficiency and robustness. |
| Approach: | They propose a generative classifier for natural language inference (NLI) they compare it to discriminative models and large-scale pretrained models like BERT . |
| Outcome: | The proposed classifier outperforms discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise. |
MABEL: Attenuating Gender Bias using Textual Entailment Data (2022.emnlp-main)
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| Challenge: | Existing methods for mitigating gender bias in language models are insufficient or inconsistent. |
| Approach: | They propose a method for attenuating gender bias using entailment labels . they use a contrastive learning objective on counterfactually augmented enanglement pairs . |
| Outcome: | The proposed method outperforms previous task-agnostic debiasing approaches on intrinsic and extrinsic metrics and preserves task performance after fine-tuning on downstream tasks. |
ConjNLI: Natural Language Inference Over Conjunctive Sentences (2020.emnlp-main)
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| Challenge: | Existing stress tests do not consider non-boolean usages of conjunctions and use templates . large-scale pre-trained models do not understand conjunctive semantics well, we find . |
| Approach: | They propose a stress-test for natural language inference over conjunctive sentences where the premise differs from the hypothesis by conjunctions removed, added, or replaced. |
| Outcome: | The proposed stress-test for natural language inference over conjunctive sentences is challenging . it finds that pre-trained models do not understand conjunction semantics well . |
Data and Representation for Turkish Natural Language Inference (2020.emnlp-main)
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| Challenge: | Large annotated datasets in NLP are overwhelmingly in English . obtaining new annotation resources for each task in each language would be prohibitively expensive . |
| Approach: | They propose to use machine translation to translate large annotated datasets into Turkish . they find that in-language embeddings are essential and morphological parsing can be avoided . |
| Outcome: | The proposed model trains on human-translated evaluation sets. |
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations (2020.lrec-1)
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Hugo Gonçalo Oliveira, João Ferreira, José Santos, Pedro Fialho, Ricardo Rodrigues, Luisa Coheur, Ana Alves
| Challenge: | a corpus of 380 domain-oriented FAQs in Portuguese is presented . paraphrases or entailed questions are created manually, by humans, or automatically, with Google Translate. |
| Approach: | They present a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. |
| Outcome: | The proposed system outperforms other systems in the domain of question-answering . it performs well when matching variations with their original questions . |
Polyglot Prompt: Multilingual Multitask Prompt Training (2022.emnlp-main)
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| Challenge: | a monolithic framework for multilingual learning can be used without any task/language-specific module. |
| Approach: | They propose a framework to exploit prompting methods for learning a unified semantic space for different languages and tasks with multilingual prompt engineering. |
| Outcome: | The proposed framework can learn tasks from different languages in a monolithic framework without any task/language-specific module. |
Prompt-based Distribution Alignment for Domain Generalization in Text Classification (2022.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) have achieved competitive performance on a range of NLP tasks. |
| Approach: | They propose to learn distributional invariance across source domains via alignment regularization loss functions to improve domain generalization by prompting. |
| Outcome: | Experiments on sentiment analysis and natural language inference show the effectiveness of the proposed method and achieve state-of-the-art results. |
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? (2024.lrec-main)
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| Challenge: | HateCheck test cases are generic and have simplistic sentence structures that do not match the real-world data. |
| Approach: | They propose a framework to generate more diverse and realistic functional tests from scratch by instructing large language models. |
| Outcome: | The proposed framework generates more diverse and realistic functional tests from scratch by instructing large language models (LLMs). |
Transparent and Coherent Procedural Mistake Detection (2025.emnlp-main)
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| Challenge: | Procedural mistake detection (PMD) is a problem of classifying whether a human user has successfully executed a task. |
| Approach: | They extend PMD to require generating visual self-dialog rationales to inform decisions . they leverage a natural language inference model to formulate two automated metrics for coherence of generated rationale. |
| Outcome: | The proposed model improves on a reframed task with a natural language inference model and a multi-faceted metrics visualization of common outcomes. |
Ecologically Valid Explanations for Label Variation in NLI (2023.findings-emnlp)
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| Challenge: | Human label variation exists in many natural language processing tasks, including NLI . |
| Approach: | They build an English dataset of 1,415 ecologically valid explanations for 122 MNLI items . they find that people can systematically vary on their interpretation . |
| Outcome: | The proposed dataset contains 1,415 ecologically valid explanations for 122 items . the results show that people can vary on interpretation and highlight differences . |
Using Natural Language Explanations to Improve Robustness of In-context Learning (2024.acl-long)
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| Challenge: | Recent studies show that large language models excel in many tasks via in-context learning (ICL). However, ICL struggles to execute complex tasks such as arithmetic, commonsense, and symbolic reasoning. |
| Approach: | They propose to augment ICL with natural language explanations (NLEs) to produce further NLEs on adversarial datasets. |
| Outcome: | The proposed approach yields more accurate results than zero-shot-ICL and using only human-generated NLEs on eight adversarial datasets. |
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation (2020.emnlp-main)
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| Challenge: | Existing benchmarks for natural language inference ignore negations and can make inferences that are difficult to make. |
| Approach: | They propose a new benchmark for natural language inference in which negation plays a critical role. |
| Outcome: | The proposed benchmarks show that negation plays a critical role in inference judgments. |
Structural Constraints and Natural Language Inference for End-to-End Flowchart Grounded Dialog Response Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to learn flowchart grounded dialogs have two limitations . Flowchart-based systems require only the chat transcripts and no additional annotations . |
| Approach: | They propose a structure-aware approach to learn flowchart grounded dialogs . it uses structural constraints derived from connectivity structure of flowchartes into a RAG framework . |
| Outcome: | The proposed approach outperforms existing approaches with a success rate of 68% and 123%. |
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation (2024.findings-acl)
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| Challenge: | Existing instruction tuning datasets are limited by the quality of the instruction tuning data. |
| Approach: | They propose a model that converts unannotated text into task-specific training datasets for instruction tuning. |
| Outcome: | The proposed model improves the performance of pretrained and instruction tuned models over the de facto self-supervised baseline. |
Evaluating the Factual Consistency of Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | a weakly-supervised approach is needed to verify factual consistency . auxiliary span extraction tasks are useful for verifying factual consistent summaries . |
| Approach: | They propose a weakly-supervised approach for verifying factual consistency . they transfer the model to summaries generated by several neural models . |
| Outcome: | The proposed approach outperforms models trained with strong supervision on source documents and human evaluations. |
Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning (2025.acl-long)
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| Challenge: | Prior studies have demonstrated that LLMs generate step-by-step rationales, but limited data is available to improve their performance in commercial settings due to copyright and legal issues. |
| Approach: | They propose a trainable framework that tunes a (small) LLM to generate outputs from a pool of diverse rationales that selectively improves the downstream task. |
| Outcome: | The proposed framework outperforms several trainable and prompting baselines on maths problem solving, natural language inference, and commonsense reasoning. |
NILE : Natural Language Inference with Faithful Natural Language Explanations (2020.acl-main)
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| Challenge: | Recent growth in popularity of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of predicted labels. |
| Approach: | They propose a novel method which generates labels along with its faithful explanations. |
| Outcome: | The proposed method is more accurate than previously reported methods and has higher sensitivity than previous methods. |
Explaining Interactions Between Text Spans (2023.emnlp-main)
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| Challenge: | Existing highlight-based explanations focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens. |
| Approach: | They propose a multi-annotator dataset of human span interaction explanations for NLU and FC. |
| Outcome: | The proposed method compares human reasoning processes to those of a fine-tuned large language model. |
Interventional Training for Out-Of-Distribution Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Existing methods for NLU training use only known and single confounders, but in many NLU tasks the confounder can be unknown and multifactorial. |
| Approach: | They propose a method that performs multi-granular intervention with identified multifactorial confounders by using a bottom-up automatic intervention method. |
| Outcome: | The proposed method performs multi-granular intervention with identified multifactorial confounders on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
Measuring Association Between Labels and Free-Text Rationales (2021.emnlp-main)
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| Challenge: | Existing models for extractive rationales do not work as well on reasoning tasks requiring free-text rationale. |
| Approach: | They propose to use pipelines to extract rationales from input words and to use them to explain reasoning tasks. |
| Outcome: | The proposed models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales. |
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models (2023.emnlp-main)
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Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa
| Challenge: | Large multilingual models rely on a single vocabulary shared across 100+ languages . this vocabulary bottleneck limits the representational capabilities of multilingual model XLM-R . |
| Approach: | They propose a new approach for scaling to large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. |
| Outcome: | The proposed model outperforms XLM-R on all language tasks and is particularly effective on low-resource tasks. |
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2023.acl-long)
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| Challenge: | despite advances in detecting fake news, there is a sizable gap between machine-generated and human-authored fake news . a nave solution is to collect human-written news articles that contain inaccurate information by crawling untrustworthy news media. |
| Approach: | They propose a framework for generating training examples informed by the styles and strategies of human-authored propaganda. |
| Outcome: | The proposed framework improves detection of human-written disinformation by 3.62–7.69% on two public datasets. |
Rethinking Annotation: Can Language Learners Contribute? (2023.acl-long)
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| Challenge: | Researchers have traditionally recruited native speakers to provide annotations for benchmark datasets, but there are languages for which recruiting native speakers is difficult. |
| Approach: | They recruit 36 language learners and provide two types of additional resources and perform mini-tests to measure their language proficiency. |
| Outcome: | The proposed method improves learners' language proficiency in terms of vocabulary and grammar. |
Macedon: Minimizing Representation Coding Rate Reduction for Cross-Lingual Natural Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing approaches to learn cross-lingual models require limited data to perform cross-linguistic tasks. |
| Approach: | They propose a method to remove language-associated information via minimizing representation coding rate reduction. |
| Outcome: | The proposed model outperforms state-of-the-art models on cross-lingual tasks. |
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference (2020.lrec-1)
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| Challenge: | Recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by looking at the hypothesis while completely ignoring the premise. |
| Approach: | They propose to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. |
| Outcome: | The proposed models can be used to mitigate the hypothesis-only bias by using down-sampling and adversarial training. |
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation (2020.lrec-1)
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| Challenge: | Monolingual phrase alignment is a fundamental problem in natural language understanding and crucial technique in various applications. |
| Approach: | They propose a simple Aligner for Phrasal Paraphrase with HIerarchical REpresentation that uses word embeddings to train phrase alignments. |
| Outcome: | The proposed algorithm outperforms the previous methods and establishes the state-of-the-art. |
Towards the Necessity for Debiasing Natural Language Inference Datasets (2020.lrec-1)
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| Challenge: | Delexicalization of datasets can improve natural language inference performance . a dataset with a delexicalized version of the FEVER dataset is used for natural language learning . |
| Approach: | They propose two techniques for delexicalization that modify annotated datasets to control the importance of lexical entities. |
| Outcome: | The proposed methods maintain performance in-domain and improve performance in some out-of-domain settings. |
Exploring the Boundaries of GPT-4 in Radiology (2023.emnlp-main)
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Qianchu Liu, Stephanie Hyland, Shruthi Bannur, Kenza Bouzid, Daniel Castro, Maria Wetscherek, Robert Tinn, Harshita Sharma, Fernando Pérez-García, Anton Schwaighofer, Pranav Rajpurkar, Sameer Khanna, Hoifung Poon, Naoto Usuyama, Anja Thieme, Aditya Nori, Matthew Lungren, Ozan Oktay, Javier Alvarez-Valle
| Challenge: | Recent success of general-domain large language models has changed the natural language processing paradigm towards a unified foundation model across domains and applications. |
| Approach: | They evaluate the performance of GPT-4 on a variety of radiology tasks . they find it outperforms or matches current SOTA radiology models . |
| Outcome: | The proposed model outperforms or matches current SOTA radiology models on a range of tasks. |
kNN-CM: A Non-parametric Inference-Phase Adaptation of Parametric Text Classifiers (2023.findings-emnlp)
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| Challenge: | Existing studies on text-discriminating properties of semi-parametric models have not been done on non-parameter models. |
| Approach: | They propose an inference-phase approach that incorporates a neighborhood search into a model to enhance the capacity of a pre-trained parametric text classifier. |
| Outcome: | The proposed model improves performance on eight SuperGLUE tasks, three adversarial natural language inference datasets, 11 question-answering (QA) datasets and two sentiment classification datasets. |
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024.emnlp-main)
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| Challenge: | Existing methods that edit large language models with updated knowledge can cause side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering. |
| Approach: | They propose to regularize the edit update weights by imposing constraints on their complexity based on the RElative Change in weighT. |
| Outcome: | The proposed method can significantly mitigate the side effects while maintaining over 94% editing performance. |
Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions (2025.findings-emnlp)
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Meghdut Sengupta, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Debanjan Ghosh, Henning Wachsmuth
| Challenge: | Metaphorical language is prevalent in everyday communication, often used unconsciously, as in "rising crime" incorporating conceptual metaphors improves performance on downstream tasks that implicitly require correct metaphor interpretation, such as NLI. |
| Approach: | They extend metaphorical texts in an existing NLI dataset by source and target domains and conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI. |
| Outcome: | The proposed method improves performance on natural language inference tasks with metaphoric input. |
Inference and Verbalization Functions During In-Context Learning (2024.findings-emnlp)
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| Challenge: | Previous work has found that, in some settings, ICL performance is minimally affected by using demonstrations with irrelevant label words. |
| Approach: | They hypothesize that large language models (LMs) perform in-context learning from a handful of demonstrations via two sequential processes: an inference function that solves the task and a verbalization function that maps the inferred answer to the label space. |
| Outcome: | The proposed model can be localized in specific layers across open-source models, including GEMMA-7B, MISTRAL-7B-V0.3, GEIMA-2-27B, and LLAMA-3.1-70B. |
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications (2024.lrec-main)
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| Challenge: | Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference data to build high-performance models can outperform conventional methods. |
| Approach: | They propose a multilingual sentence embedding model by extending an existing monolingual model by using the low-rank adaptation technique. |
| Outcome: | The proposed model outperforms the previous approach and shows that languages with fewer resources or those with less linguistic similarity to English benefit more from the parameter increase. |
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations. |
| Approach: | They propose a framework that reformulates retrieval and generation as constrained optimization and path planning. |
| Outcome: | The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations. |
MATCHA: Matching Text via Contrastive Semantic Alignment (2026.findings-acl)
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| Challenge: | MATCHA is an automatic metric that rewards semantic agreement with a reference and penalizes contradictions. |
| Approach: | They introduce a metric that jointly rewards semantic agreement with a reference and penalizes contradictions. |
| Outcome: | The proposed metric outperforms popular metrics on eight public benchmarks compared with human annotations on question-answering, image caption generation, natural language inference, summarization, and semantic textual similarity tasks. |
New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French (2024.lrec-main)
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| Challenge: | DACCORD is a dataset for automatic detection of contradictions between sentences . it is the first dataset exclusively dedicated to this task . |
| Approach: | They introduce DACCORD, a dataset in French for automatic detection of contradictions between sentences. |
| Outcome: | The proposed datasets are more challenging than existing datasets for the mainstream task in French. |
VerifyMatch: A Semi-Supervised Learning Paradigm for Natural Language Inference with Confidence-Aware MixUp (2024.emnlp-main)
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| Challenge: | Natural language inference (NLI) is a key task for evaluating a model's ability to perform natural language understanding and reasoning. |
| Approach: | They propose to construct pseudo-generated samples using class-specific fine-tuned large language models (LLMs) . they retain all pseudo-labeled samples, but use MixUp to ensure unlabele . |
| Outcome: | The proposed approach achieves competitive accuracy compared to strong baselines for NLI datasets in low-resource settings. |
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. |
| Approach: | They propose a pruning-based approach to balance CF and downstream task performance by integrating the ratio of the task vector to pre-trained model parameters into the pruning criteria. |
| Outcome: | The proposed pruning-based approach limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. |
Language Fusion for Parameter-Efficient Cross-lingual Transfer (2025.acl-long)
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| Challenge: | Limited availability of multilingual text corpora for pretraining results in poor performance on downstream tasks due to undertrained representation spaces for languages other than English. |
| Approach: | They propose a method that integrates source and target language representations within low-rank (LoRA) adapters using lightweight linear transformations to enhance representation quality and transfer performance for languages other than English. |
| Outcome: | The proposed method improves representation quality and performance for languages other than English while maintaining parameter efficiency. |
ALVIN: Active Learning Via INterpolation (2024.emnlp-main)
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| Challenge: | Experimental results show that Active Learning methods ignore example groups whose prevalence may vary . supervised fine-tuning remains a critical component of model development, authors say . |
| Approach: | They propose an approach that uses interpolations to create anchors between examples . they propose to use the model to identify informative examples that counteract shortcuts . |
| Outcome: | The proposed model outperforms state-of-the-art active learning methods on six datasets . it prioritizes high-certainty instances that integrate representations from different example groups . |
Incorporating Temporal Coherence to Cross-Document Event Coreference Resolution (2026.acl-long)
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| Challenge: | Existing approaches focus on enhancing semantic coherence between event mentions, but they overlook the critical aspect of temporal coherency. |
| Approach: | They propose a Temporal Cohorence-driven event coreference framework that explicitly models temporal constraints by constructing a temporal event graph and a GNN to resolve conflicts. |
| Outcome: | Experiments on the ECB+, GVC, WEC, and ECb+META datasets show that CohTP outperforms state-of-the-art methods. |
ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code (2026.findings-acl)
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| Challenge: | Large Reasoning Models suffer from the "over-thinking" problem, causing performance degradation. |
| Approach: | They propose a unified model that balances reasoning performance and efficiency across multiple formats through a reinforcement learning framework augmented with length-aware optimization. |
| Outcome: | The proposed model reduces token costs while preserving performance compared to traditional models. |
To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation (2024.lrec-main)
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| Challenge: | State-of-the-art language models perform well on a variety of language tasks, but struggle with understanding negation cues in tasks like natural language inference (NLI). |
| Approach: | They propose a new learning strategy for negation building on ELECTRA’s replaced token detection objective. |
| Outcome: | The proposed approach leads to substantial gains on a variant of RTE with additional negation. |
Introducing Verification Task of Set Consistency with Set-Consistency Energy Networks (2025.acl-long)
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| Challenge: | Traditional methods that rely on 1:1 pairwise comparisons fail to capture inconsistencies . few studies have addressed related problems in the domain of factual inconsistenency detection . |
| Approach: | They propose a set-consistency verification task that assesses logical coherence of entire sets . they propose 'set-consistent energy network' that employs a margin-based loss to learn the compatibility among a collection of statements . |
| Outcome: | The proposed model outperforms existing methods and significantly outperformed existing models. |
Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) produce incomplete or selectively omit key information . omissions of key information or misrepresentation of conflicting evidence can cause harm . |
| Approach: | They propose a method that decomposes texts into atomic statements and uses natural language inference to identify missing facts and a Q A-based metric that extracts question-answer pairs and compares responses across sources. |
| Outcome: | The proposed evaluation metrics show they perform better than more complex metrics, but at a cost. |