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Recent literature focuses on utilizing the entity information in thesentence-level relation extraction (RE), but this risks leaking superficial andspurious clues of relations. As a result, RE still suffers from unintendedentity bias, i.e., the spurious correlation between entity mentions (names) andrelations. Entity bias can mislead the RE models to extract the relations thatdo not exist in the text. To combat this issue, some previous work masks theentity mentions to prevent the RE models from overfitting entity mentions.However, this strategy degrades the RE performance because it loses thesemantic information of entities. In this paper, we propose the CORE(Counterfactual Analysis based Relation Extraction) debiasing method thatguides the RE models to focus on the main effects of textual context withoutlosing the entity information. We first construct a causal graph for RE, whichmodels the dependencies between variables in RE models. Then, we propose toconduct counterfactual analysis on our causal graph to distill and mitigate theentity bias, that captures the causal effects of specific entity mentions ineach instance. Note that our CORE method is model-agnostic to debias existingRE systems during inference without changing their training processes.Extensive experimental results demonstrate that our CORE yields significantgains on both effectiveness and generalization for RE. The source code isprovided at: https://github.com/vanoracai/CoRE.

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis