Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines.

翻译：关系抽取旨在根据两个实体的上下文提取潜在关系，因此从句子中推导合理语境发挥着重要作用。以往的研究要么聚焦于利用实体信息（如实体类型、实体表述）进行关系推理而忽略上下文内容，要么采用反事实思维消除模型对实体潜在关系的偏见，但关系推理过程仍会受无关内容干扰。因此，如何保留相关内容并剔除句子中的噪声片段至关重要。此外，保留内容需保持足够流畅以维持语义连贯性与可解释性。本文提出一种名为RE2的新型依据提取框架，通过引入连续性与稀疏性两个因子，从句子中获取相关且连贯的依据。针对黄金依据未标注的问题，RE2对句子中每个词元应用可优化二元掩码，并根据关系标签调整需选择的依据。四个数据集上的实验表明，RE2超越了基线方法。