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    <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.</abstract>
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%0 Conference Proceedings
%T Uncovering Main Causalities for Long-tailed Information Extraction
%A Nan, Guoshun
%A Zeng, Jiaqi
%A Qiao, Rui
%A Guo, Zhijiang
%A Lu, Wei
%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 nan-etal-2021-uncovering
%X 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.
%R 10.18653/v1/2021.emnlp-main.763
%U https://aclanthology.org/2021.emnlp-main.763/
%U https://doi.org/10.18653/v1/2021.emnlp-main.763
%P 9683-9695

Markdown (Informal)

[Uncovering Main Causalities for Long-tailed Information Extraction](https://aclanthology.org/2021.emnlp-main.763/) (Nan et al., EMNLP 2021)

• Uncovering Main Causalities for Long-tailed Information Extraction (Nan et al., EMNLP 2021)

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.