Papers with textual entailment task
BelarusianGLUE: Towards a Natural Language Understanding Benchmark for Belarusian (2025.acl-long)
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| Challenge: | Recent advances in NLP, such as large language models, have had groundbreaking impact on the field. |
| Approach: | They propose a benchmark for Belarusian, an East Slavic language, with 15K instances in five tasks: sentiment analysis, linguistic acceptability, word in context, Winograd schema challenge, textual entailment. |
| Outcome: | The proposed model underperforms on sentiment analysis, linguistic acceptability, word in context, Winograd schema challenge and textual entailment, but is competitive for linguistic acceptance. |
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning (2021.naacl-main)
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| Challenge: | Existing zero-shot learning methods for multi-label text classification mostly learn a matching model between the feature space of text and the label space. |
| Approach: | They propose to use a graph encoder to incorporate label hierarchies to learn effective label representations on the zero-shot multi-label text classification problem. |
| Outcome: | The proposed approach outperforms previous non-pretrained methods on the zero-shot multi-label text classification task. |
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting. |
| Approach: | They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data. |
| Outcome: | The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset. |
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News (2021.eacl-main)
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| Challenge: | Exaggerations in health news can have tremendous adverse effects on the lifestyle of the common masses who feed themselves mostly on such news instead of the source scientific publication. |
| Approach: | They propose a three-step approach that extracts relation phrases from a scientific paper and then classifies the strength of the relationship phrase extracted. |
| Outcome: | The proposed approach outperforms baseline models that compare state-of-the-art embedding of the statement pairs through a binary classifier or recast the problem as a textual entailment task. |
Neural Natural Logic Inference for Interpretable Question Answering (2021.emnlp-main)
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| Challenge: | Existing question answering models are based on textual entailment tasks . prior work has focused on QA on premise-based questions . |
| Approach: | They propose a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures towards developing effective question answering models. |
| Outcome: | The proposed model outperforms previous work on multiple-choice science questions . it integrates natural logic reasoning within deep learning architectures to build proof paths . |