Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Distant supervision models suffer from high label noise and are not reliable for DS. |
| Approach: | They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF. |
| Outcome: | The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function. |
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| Challenge: | Existing approaches to reducing wrongly labeled instances are based on a bag-level setting . however, sentence-level training is vulnerable to the noise brought by DS, which limits its application. |
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GATEAU: Selecting Influential Samples for Long Context Alignment (2025.emnlp-main)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Yunshui Li, Kangyang Luo, Chuancheng Lv, Kaikai An, Fanchao Qi, Baobao Chang, Maosong Sun
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