  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.
