De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention

Wenkai Zhang, Hongyu Lin, Xianpei Han, Le Sun


Abstract
Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching. Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and therefore undermines the effectiveness and the robustness of the learned models. In this paper, we fundamentally explain the dictionary bias via a Structural Causal Model (SCM), categorize the bias into intra-dictionary and inter-dictionary biases, and identify their causes. Based on the SCM, we learn de-biased DS-NER via causal interventions. For intra-dictionary bias, we conduct backdoor adjustment to remove the spurious correlations introduced by the dictionary confounder. For inter-dictionary bias, we propose a causal invariance regularizer which will make DS-NER models more robust to the perturbation of dictionaries. Experiments on four datasets and three DS-NER models show that our method can significantly improve the performance of DS-NER.
Anthology ID:
2021.acl-long.371
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4803–4813
Language:
URL:
https://aclanthology.org/2021.acl-long.371
DOI:
10.18653/v1/2021.acl-long.371
Bibkey:
Cite (ACL):
Wenkai Zhang, Hongyu Lin, Xianpei Han, and Le Sun. 2021. De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4803–4813, Online. Association for Computational Linguistics.
Cite (Informal):
De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention (Zhang et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.371.pdf
Video:
 https://aclanthology.org/2021.acl-long.371.mp4
Code
 zwkatgithub/DSCAU
Data
CoNLL-2003