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Emotion-cause pair extraction (ECPE) aims to extract emotion clauses andcorresponding cause clauses, which have recently received growing attention.Previous methods sequentially encode features with a specified order. Theyfirst encode the emotion and cause features for clause extraction and thencombine them for pair extraction. This lead to an imbalance in inter-taskfeature interaction where features extracted later have no direct contact withthe former. To address this issue, we propose a novel Pair-Based Joint Encoding(PBJE) network, which generates pairs and clauses features simultaneously in ajoint feature encoding manner to model the causal relationship in clauses. PBJEcan balance the information flow among emotion clauses, cause clauses andpairs. From a multi-relational perspective, we construct a heterogeneousundirected graph and apply the Relational Graph Convolutional Network (RGCN) tocapture the various relationship between clauses and the relationship betweenpairs and clauses. Experimental results show that PBJE achievesstate-of-the-art performance on the Chinese benchmark corpus.

Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction