Abstract:Recently, graph neural networks (GNN), due to their compelling representation learning ability, have been exploited to deal with emotion-cause pair extraction (ECPE). However, current GNN-based ECPE methods mostly concentrate on modeling the local dependency relation between homogeneous nodes at the semantic granularity of clauses or clause pairs, while they fail to take full advantage of the rich semantic information in the document. To solve this problem, we propose a novel hierarchical heterogeneous graph a… Show more
“…Yu et al [18] proposed a novel hierarchical heterogeneous graph attention network to model global semantic relations among nodes for emotion-cause pair extraction (ECPE). This method introduced all types of semantic elements involved in ECPE.…”
“…Yu et al [18] proposed a novel hierarchical heterogeneous graph attention network to model global semantic relations among nodes for emotion-cause pair extraction (ECPE). This method introduced all types of semantic elements involved in ECPE.…”
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