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        <title>Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention</title>
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        <namePart type="given">Jiawei</namePart>
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    <abstract>Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view. By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers. To resolve this problem, we propose to intervene on the context via backdoor adjustment during training. Experiments show that our method significantly improves the FSED on both ACE05 and MAVEN datasets.</abstract>
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%0 Conference Proceedings
%T Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention
%A Chen, Jiawei
%A Lin, Hongyu
%A Han, Xianpei
%A Sun, Le
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F chen-etal-2021-honey
%X Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view. By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers. To resolve this problem, we propose to intervene on the context via backdoor adjustment during training. Experiments show that our method significantly improves the FSED on both ACE05 and MAVEN datasets.
%R 10.18653/v1/2021.emnlp-main.637
%U https://aclanthology.org/2021.emnlp-main.637
%U https://doi.org/10.18653/v1/2021.emnlp-main.637
%P 8078-8088

Markdown (Informal)

[Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention](https://aclanthology.org/2021.emnlp-main.637) (Chen et al., EMNLP 2021)

• Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention (Chen et al., EMNLP 2021)

ACL

• Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2021. Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8078–8088, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.