  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
task. Our EmotionIC consists of three main components, i.e., Identity Masked
Multi-Head Attention (IMMHA), 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 complex 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.
