Abstract
Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference. Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions. Experiments on three IE tasks across five public datasets show the effectiveness of our CFIE model in mitigating the spurious correlation issues.- Anthology ID:
- 2021.emnlp-main.763
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9683–9695
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.763
- DOI:
- 10.18653/v1/2021.emnlp-main.763
- Cite (ACL):
- Guoshun Nan, Jiaqi Zeng, Rui Qiao, Zhijiang Guo, and Wei Lu. 2021. Uncovering Main Causalities for Long-tailed Information Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9683–9695, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Uncovering Main Causalities for Long-tailed Information Extraction (Nan et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.emnlp-main.763.pdf
- Code
- heyyyyyyg/cfie
- Data
- MAVEN, OntoNotes 5.0