A multi-level multi-task progressive framework based on relational graph convolutional networks for causal emotion entailment

Changzhi Zhou, Dandan Song, Zhijing Wu, Linmei Hu, Jing Xu, Yanru Zhou

Abstract: Causal Emotion Entailment (CEE) aims to identify the corresponding causal utterances given a non-neutral target utterance and its conversational history. Previous studies have been devoted to modeling conversational context by infusing emotion and speaker information separately. However, for target utterances with different emotions, the joint distributions of speaker identities and emotion types for corresponding causal utterances are significantly different. In addition, a single non-neutral target utterance may correspond to multiple causal utterances and vice versa (i.e., one-to-many and many-to-one relations). These factors make the corresponding relation between the non-neutral target utterances and causal utterances more diverse and difficult to model, hindering the performance of existing methods. To this end, we design an Utterance-level Interaction Graph (UIG) and a Pair-level Interaction Graph (PIG). The former explicitly captures differences in the joint distribution of speaker identities and emotion types for target utterances with different emotions by utterance-level interactions. The latter models the correlations between utterances resulting from one-to-many and many-to-one relations by pair-level interactions. Furthermore, we propose a Multi-level Multi-task Progressive Framework (MMPF) for the CEE task, which achieves UIG and PIG using Relational Graph Convolutional Networks. Extensive experiments on RECCON-DD datasets demonstrate that MMPF obtains state-of-the-art performance.