A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction

TL;DR

This paper introduces a novel multi-turn machine reading comprehension framework with a rethink mechanism for emotion-cause pair extraction, effectively modeling complex relations and incorporating semantic information, outperforming existing methods.

Contribution

It transforms ECPE into a document-level MRC task and proposes a multi-turn framework with a rethink mechanism to address label sparsity and relation modeling issues.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively models complex emotion-cause relations.

Incorporates explicit semantic information flow.

Abstract

Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-cause pairs from an emotional document. Most recent studies use end-to-end methods to tackle the ECPE task. However, these methods either suffer from a label sparsity problem or fail to model complicated relations between emotions and causes. Furthermore, they all do not consider explicit semantic information of clauses. To this end, we transform the ECPE task into a document-level machine reading comprehension (MRC) task and propose a Multi-turn MRC framework with Rethink mechanism (MM-R). Our framework can model complicated relations between emotions and causes while avoiding generating the pairing matrix (the leading cause of the label sparsity problem). Besides, the multi-turn structure can fuse explicit semantic information flow between emotions and causes. Extensive experiments on the benchmark emotion cause corpus demonstrate the effectiveness of our proposed framework, which outperforms existing state-of-the-art methods.