Papers with RTE
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity (2021.emnlp-main)
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| Challenge: | Multi-task auxiliary learning uses a set of relevant auxiliary tasks to improve performance of a primary task. |
| Approach: | They propose a time-efficient sampling method to select the most beneficial sub-datasets from the auxiliary tasks to achieve efficient multi-task auxiliary learning. |
| Outcome: | The proposed method significantly outperforms random sampling and ST-DNN on three benchmark datasets. |
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning (N18-2)
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| Challenge: | Existing methods to recognize textual entailment use a CCG parser to process sentences . failing to recognize the similar syntactic structure results in inconsistent argument structures . |
| Approach: | They propose to extend existing CCG parsers to parse sentences consistently . they use an inter-sentence modeling with Markov Random Fields to achieve this . |
| Outcome: | The proposed method improves on English and Japanese languages. |
Acquisition of Phrase Correspondences Using Natural Deduction Proofs (N18-1)
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| Challenge: | Existing methods for Recognizing Textual Entailment (RTE) lack phrasal knowledge. |
| Approach: | They propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. |
| Outcome: | The proposed method detects paraphrases that are absent from existing paraphrase databases and improves accuracy of RTE tasks. |
Evaluating Paraphrastic Robustness in Textual Entailment Models (2023.acl-short)
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| Challenge: | Recognizing Textual Entailment models understand language and should be robust to paraphrases. |
| Approach: | They propose to evaluate whether RTE models are robust to paraphrase . they use 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate their models . |
| Outcome: | The evaluation set shows that models change predictions on 8-16% of paraphrased examples, suggesting that there is room for improvement. |
Yes, No or IDK: The Challenge of Unanswerable Yes/No Questions (2022.naacl-main)
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| Challenge: | Existing datasets for Yes/No QA are lacking information needed to answer a Yes/Non question. |
| Approach: | They extend the Yes/No QA task by adding questions with an IDK answer to a BoolQ dataset and create out-of-domain test sets for the task. |
| Outcome: | The proposed dataset includes paragraphs together with naturally occurring questions whose answer is either "Yes" or "No". |
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)
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| Challenge: | Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model . |
| Approach: | They propose to transfer knowledge from discourse markers to augment the quality of the NLI model. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets. |
RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods focus on instance-level editing, which is prone to knowledge degradation and general ability deterioration due to redundant instance-specific modifications. |
| Approach: | They propose a rule-level editing method that generalizes rule-derived knowledge to update rule-based instances. |
| Outcome: | The proposed method improves portability and performance over baselines for LLaMA-2-7B on RULEmix. |
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English (2022.findings-acl)
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| Challenge: | Existing research on cultural background modeling is coarse-grained and does not examine cultural differences among speakers of the same language. |
| Approach: | They use a news-based cultural background prediction dataset to annotate, validate and benchmark NLP models with cultural background features. |
| Outcome: | The proposed model improves on nine syntactic, semantic, and psycholinguistic tasks while introducing cultural background information does not improve the Go-Emotions task due to text domain conflicts. |
Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment (L18-1)
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| Challenge: | a method to improve the quality of training data is needed . annotation errors of dialog act corpus mislead learning results of Bayesian network . |
| Approach: | They propose to introduce a null hypothesis for predictability of textual entailment labels and test it using a Naive Bayes model. |
| Outcome: | The proposed method does not reject the null hypothesis, but it improves on the existing models. |
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations (2021.findings-emnlp)
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| Challenge: | SupCL-Seq extends contrastive learning from computer vision to sequence classification tasks. |
| Approach: | They propose a supervised alternative to Masked Language Modeling (MLM) that extends contrastive learning to sequence optimization in NLP by altering the dropout mask probability in standard Transformer architectures. |
| Outcome: | The proposed method leads to large gains on the GLUE benchmark, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B. |
Figurative Language in Recognizing Textual Entailment (2021.findings-acl)
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| Challenge: | Existing RTE models struggle to capture figurative language, despite its ubiquity, it remains a bottleneck in automatic text understanding. |
| Approach: | They propose to frame five existing figurative language datasets into over 12,500 RTE examples. |
| Outcome: | The proposed models struggle to perform pragmatic inference and reasoning about world knowledge. |
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
| Approach: | They propose an effective transfer learning approach for cross-lingual NLI . they perform experiments on English-Hindi language pairs in cross-linguistic setting . |
| Outcome: | The proposed model improves the baseline model by 10% over the state-of-the-art model. |
On the Importance of Delexicalization for Fact Verification (D19-1)
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| Challenge: | Neural networks (NNs) perform state-of-the-art (SOA) performance in many complex tasks. |
| Approach: | They investigate the importance that a model assigns to various aspects of data . they experiment with two strategies of masking to mitigate this dependence on lexicalized information . |
| Outcome: | The proposed model improves on the in-domain dataset by 10% compared to the fully lexicalized model. |
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotations for NLI tasks. |
| Approach: | They propose a way to incorporate unlabeled data into semi-supervised learning (SSL) using a conditional language model, they propose to generate hypotheses for unlabed sentences . |
| Outcome: | The proposed framework significantly improves the performance of four NLI datasets in low-resource settings. |
RTE-GMoE: A Model-agnostic Approach for Relation Triplet Extraction via Graph-based Mixture-of-Expert Mutual Learning (2025.emnlp-main)
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| Challenge: | Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition. |
| Approach: | They propose a mutual learning framework for Relation Triplet Extraction to address this limitation. |
| Outcome: | The proposed framework improves on four state-of-the-art backbones and benchmarks. |
Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0 (2021.findings-emnlp)
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| Challenge: | Recent work has attempted to improve extractive QA performance by enriching the dataset with unanswerable questions. |
| Approach: | They build an out-of-domain corpus of competitive and non-competitive questions . they compare the results with the results of the Recognizing Textual Entailments task . |
| Outcome: | The proposed model fails even in the case of simpler questions . the proposed model can be used to address more realistic situations in reading comprehension . |
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models (2020.acl-main)
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| Challenge: | Recent models for unsupervised representation learning of text have put little focus on discourse-level representations. |
| Approach: | They propose an inter-sentence objective for pretraining language models that models discourse coherence and the distance between sentences. |
| Outcome: | The proposed model outperforms the BERT-Large model on the discourse representation benchmark DiscoEval and yields gains of 2%-6% absolute even for tasks that do not explicitly evaluate discourse. |
Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks (P19-1)
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| Challenge: | Existing domain adaptation techniques for question deduplication and RTE focus on transferring category independent knowledge between domains. |
| Approach: | They propose to use gradient reversal to explicitly learn shared and unshared (domain specific) representations between two textual domains to compensate for domain mismatch while distilling domain specific knowledge. |
| Outcome: | The proposed approach outperforms other methods on question deduplication and on recognizing textual entailment tasks, while still distilling domain specific knowledge. |
OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)
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| Challenge: | Existing pipelines for relational triple extraction are underutilizing regional information of triple. |
| Approach: | They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection . |
| Outcome: | The proposed framework could extract all types of triples on two widely used datasets. |
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting. |
| Approach: | They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting. |
| Outcome: | The proposed model outperforms existing methods under zero-shot setting on three datasets. |
Recognizing Value Resonance with Resonance-Tuned RoBERTa Task Definition, Experimental Validation, and Robust Modeling (2024.lrec-main)
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Noam K. Benkler, Scott Friedman, Sonja Schmer-Galunder, Drisana Marissa Mosaphir, Robert P. Goldman, Ruta Wheelock, Vasanth Sarathy, Pavan Kantharaju, Matthew D. McLure
| Challenge: | Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts is a fundamental goal of social anthropology. |
| Approach: | They propose to use a novel hand-annotated dataset and a transformer-based model to model the NLP task of Recognizing Value Resonance (RVR) they extend existing work by refining the task definition and releasing the WVC dataset. |
| Outcome: | The proposed models outperform top-performing Recognizing Textual Entailment models in recognizing value resonance and zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. |
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. |
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction (2024.lrec-main)
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| Challenge: | Existing methods for relational triple extraction (RTE) are unnatural and recast RTE tasks to text-to-text prompting formats. |
| Approach: | They propose a tabular prompting for RTE which frames RTE task into a table generation task and propose an instructive in-context learning which only selects and annotates samples considering triple semantics in massive unlabeled samples. |
| Outcome: | The proposed prompting for RTE with TableIE achieves state-of-the-art performance compared to other methods . the proposed prompts are based on off-the shelf LLMs and are scalable to multiple scenarios . |