  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.
