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.

翻译：情感成因提取(ECPE)是情感成因分析中的一项新兴任务,它从情感成因文件中提取出潜在的情感成因配对。最近的大多数研究都采用端对端方法来处理ECPE的任务。然而,这些方法要么存在标签偏狭问题,要么没有在情感和原因之间建立复杂的关系模式。此外,它们都不考虑条款的明确语义信息。为此,我们将ECPE的任务转换成文件级机器阅读(MRC)的任务,并提出了带有再思考机制的多方向 MRC 框架。 我们的框架可以建模情感和原因之间的复杂关系,同时避免生成配对矩阵(标签宽度问题的主要原因 ) 。 此外,多方向结构可以整合情感和原因之间的明确的语义信息流动。 有关基准情感致因的大规模实验证明了我们拟议框架的有效性,它超越了现有的状态方法。