An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft Sharing

Emotion–cause pair extraction (ECPE), i.e., extracting pairs of emotions and corresponding causes from text, has recently attracted a lot of research interest. However, current ECPE models face two problems: (1) The common two-stage pipeline causes the error to be accumulated. (2) Ignoring the mutual connection between the extraction and pairing of emotion and cause limits the performance. In this paper, we propose a novel end-to-end mutually interactive emotion–cause pair extractor (Emiece) that is able to effectively extract emotion–cause pairs from all potential clause pairs. Specifically, we design two soft-shared clause-level encoders in an end-to-end deep model to measure the weighted probability of being a potential emotion–cause pair. Experiments on standard ECPE datasets show that Emiece achieves drastic improvements over the original two-step ECPE model and other end-to-end models in the extraction of major emotional cause pairs. The effectiveness of soft sharing and the applicability of the Emiece framework are further demonstrated by ablation experiments.