CoVariance-based Causal Debiasing for Entity and Relation Extraction

Lin Ren, Yongbin Liu, Yixin Cao, Chunping Ouyang

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Submission Track 2: Interpretability, Interactivity, and Analysis of Models for NLP

Keywords: Causal Debiasing, Entity and Relation Extraction, Covariance Optimizing, Variance Optimizing

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 c$\underline{\textbf{o}}$variance and $\underline{\textbf{v}}$ariance $\underline{\textbf{o}}$ptimization framework (OVO) to optimize feature representations and conduct general debiasing. In particular, the proposed $\underline{\textbf{c}}$ovariance $\underline{\textbf{op}}$timizing (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 $\\underline{\textbf{v}}$ariance $\underline{\textbf{op}}$timizing (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.

Submission Number: 3699