Rule Discovery for Natural Language Inference Data Generation Using Out-of-Distribution Detection (2025.emnlp-main)
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| Challenge: | Existing training rules for natural language inference do not cover the diversity of natural language. |
| Approach: | They propose a framework that combines out-of-distribution detection and clustering to identify new premise–hypothesis pairs in a dataset that are not covered by existing rules. |
| Outcome: | The proposed framework achieves +0.85%p accuracy on 2k and +0.15%p on 550k samples. |
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
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