Emotion Recognition in Conversation from Variable-Length Context

Existing approaches to Emotion Recognition in Conversation (ERC) use a fixed context window to recognize speakers' emotion, which may lead to either scantiness of key context or interference of redundant context. In response, we explore the benefits of variable-length context and propose a more effective approach to ERC. In our approach, we leverage different context windows when predicting the emotion of different utterances. New modules are included to realize variable-length context: 1) two speaker-aware units, which explicitly model inner- and inter-speaker dependencies to form distilled conversational context and 2) a top-k normalization layer, which determines the most proper context windows from the conversational context to predict emotion. Experiments and ablation study show that our approach outperforms several strong baselines on three public datasets.