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

• URL: http://arxiv.org/abs/2212.01844v1
• Date: Sun, 4 Dec 2022 15:24:14 GMT
• Title: Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction
• Authors: Junlong Liu, Xichen Shang, Qianli Ma
• Abstract summary: Methods sequentially encode features with a specified order. They first encode emotion and cause features for clause extraction and then combine them for pair extraction. This lead to an imbalance in inter-task feature interaction where features extracted later have no direct contact with the former. We propose a novel Pair-Based Joint. Network, which generates pairs and clauses simultaneously in a joint feature manner to model the causal clauses. Experiments show PBN achieves state-of-the-art performance on the Chinese benchmark corpus.
• Score: 25.101027960035147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion-cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses, which have recently received growing attention. Previous methods sequentially encode features with a specified order. They first encode the emotion and cause features for clause extraction and then combine them for pair extraction. This lead to an imbalance in inter-task feature interaction where features extracted later have no direct contact with the former. To address this issue, we propose a novel Pair-Based Joint Encoding (PBJE) network, which generates pairs and clauses features simultaneously in a joint feature encoding manner to model the causal relationship in clauses. PBJE can balance the information flow among emotion clauses, cause clauses and pairs. From a multi-relational perspective, we construct a heterogeneous undirected graph and apply the Relational Graph Convolutional Network (RGCN) to capture the various relationship between clauses and the relationship between pairs and clauses. Experimental results show that PBJE achieves state-of-the-art performance on the Chinese benchmark corpus.

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