Abstract
Emotion recognition in conversation(ERC)has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies.In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC)for ERC tasks.Our EmotionIC consists of three main components,i.e.,identity masked multi-head attention(IM-MHA),dialogue-based gated recurrent unit(DiaGRU),and skip-chain conditional random field(SkipCRF).Compared to previous ERC models,EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels.The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level.Specifically,IMMHA is applied to capture identity-based global contextual dependencies,while DiaGRU is utilized to extract speaker-and temporal-aware local contextual information.At the classification level,SkipCRF can explicitly mine com-plex emotional flows from higher-order neighboring utterances in the conversation.Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets.The ablation studies confirm that our modules can effectively model emotional inertia and contagion.
基金项目
National Natural Science Foundation of China(62236005)
National Natural Science Foundation of China(61936004)
National Natural Science Foundation of China(U1913602)
维普
万方数据