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