Experiencer-Driven and Knowledge-Aware Graph Model for emotion-cause pair extraction

Numerous approaches have been explored to learn relations between emotion and cause clauses as a key step in the extraction of emotion-cause pairs. Despite their effectiveness, there are some limitations of previous studies: (1) they ignored that the emotion experiencer is an important clue implicating causality between clauses and (2) ignored that causal commonsense as prior knowledge can enhance semantic associations between clauses. In this paper, we propose a novel Experiencer-Driven and Knowledge -Aware Graph Model (EDKA-GM). For the first limitation, we introduce an experiencer identification task and present a document-level heterogeneous graph network for capturing global experiencer information to enrich experiencer-based cross-clause association. For the second, we retrieve the causal commonsense from the ATOMIC knowledge base for each clause and establish a knowledge-aware graph network to further enhance the inter-clause relationship by modeling a full connection graph of clauses with commonsense knowledge. We are the first to explore the influence of experiencer on emotion-cause pair extraction. On a benchmark dataset, our approach performs better than competitive baselines, achieving new state-of-the-art performance.& COPY; 2023 Published by Elsevier B.V.