Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2023

Abstract

Joint entity and relation extraction tasks aim to recognize named entities and extract relations simultaneously. Suffering from a variety of data biases, such as data selection bias, and distribution bias (out of distribution, long-tail distribution), serious concerns can be witnessed to threaten the model’s transferability, robustness, and generalization. In this work, we address the above problems from a causality perspective. We propose a novel causal framework called covariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. In particular, the proposed covariance optimizing (COP) minimizes characterizing features’ covariance for alleviating the selection and distribution bias and enhances feature representation in the feature space. Furthermore, based on the causal backdoor adjustment, we propose variance optimizing (VOP) separates samples in terms of label information and minimizes the variance of each dimension in the feature vectors of the same class label for mitigating the distribution bias further. By applying it to three strong baselines in two widely used datasets, the results demonstrate the effectiveness and generalization of OVO for joint entity and relation extraction tasks. Furthermore, a fine-grained analysis reveals that OVO possesses the capability to mitigate the impact of long-tail distribution. The code is available at https://github.com/HomuraT/OVO.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10

First Page

2627

Last Page

2640

Identifier

10.18653/v1/2023.findings-emnlp.173

Publisher

Association for Computational Linguistics

City or Country

Singapore

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.18653/v1/2023.findings-emnlp.173

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