Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition
Dongming Sheng, Dong Wang, Ying Shen, Hai-Tao Zheng, Haozhuang Liu · 2020
Conversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications.Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances.Phrases carry sentiments when they are referred to emotional events under certain topics, providing a global semantic connection between utterances throughout the entire conversation.In this work, we propose a two-stage Summarization and Aggregation Graph Inference Network (SumAggGIN), which seamlessly integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion.Topic-related emotional phrases, which constitutes the global topic-related emotional connections, are recognized by our proposed heterogeneous Summarization Graph.Local dependencies, which captures short-term emotional effects between neighbouring utterances, are further injected via an Aggregation Graph to distinguish the subtle differences between utterances containing emotional phrases.The two steps of graph inference are tightly-coupled for a comprehensively understanding of emotional fluctuation.Experimental results on three CER benchmark datasets verify the effectiveness of our proposed model, which outperforms the stateof-the-art approaches.