XplaiNLI: Explainable Natural Language Inference through Visual Analytics (2020.coling-demos)
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| Challenge: | Recent research has revealed some heuristics and biases of natural language inference models. |
| Approach: | They propose an interactive visualization interface that computes Natural Language Inference with different methods and provides explanations for the decisions made by each approach. |
| Outcome: | The proposed interface computes Natural Language Inference (NLI) with three different approaches and provides explanations for the decisions made by each approach. |
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Deep Learning for Natural Language Inference (N19-5)
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
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| Challenge: | Existing datasets for natural language inference (NLI) are limited to English and a few other well-resourced languages. |
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| Challenge: | Understanding figurative language is a difficult area in NLP but is essential for proper understanding. |
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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 . |
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The Why and The How: A Survey on Natural Language Interaction in Visualization (2022.naacl-main)
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| Challenge: | Recent research shows that different forms of natural language-based interaction prove suitable to support users in accomplishing various visualization tasks. |
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Entailed Between the Lines: Incorporating Implication into NLI (2025.acl-long)
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Shreya Havaldar, Hamidreza Alvari, John Palowitch, Mohammad Javad Hosseini, Senaka Buthpitiya, Alex Fabrikant
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| Challenge: | Clinical trial reports (CTRs) are indispensable for the development of personalized medicine. |
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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. |
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