A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction (2022)

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.

## introduction emotion cause extraction (ece) is a classical emotion cause analysis task that aims to extract the corresponding causes of the given emotional expressions in an emotional document @xcite . however, the ece task is not practical in real-world scenarios without annotated emotions. to overcome the limitation and capture mutual indications of emotions and causes together, @xcite came up with a new task called emotion-cause pair extraction (ecpe), which aims to extract a potential emotion-cause pair set consisting of all emotions and their corresponding causes from a document. as shown in figure 1 (a), c2 is c1: mike said, c2: i am so happy (emotion), c3: because i have my own family now (cause), c4: i thank all the people who have helped me (emotion, cause). ## conclusions this study transforms the emotion-cause pair extraction (ecpe) task into the machine reading comprehension (mrc) task and proposes a multi-turn mrc with rethink mechanism (mm-r). this structure, which extracts emotions and causes in turn, avoids the label sparsity problem and models the complicated corresponding relations between emotions and causes. in every turn, explicit semantic information can be used effectively. furthermore, the rethink mechanism verifies each candidate emotioncause pair by modeling the flow of information from causes to emotions. experimental results on the ecpe corpus demonstrated the effectiveness of the proposed model.