Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness.

Emotion detection in conversations has become a very important and challenging task. Most of previous studies do not distinguish different speakers in a dialogue and fail to characterize inter-speaker dependencies. In this paper, we propose S peaker I nfluence-aware N eural N etwork model (SINN) to predict the emotion of the last utterance in a conversation, which explicitly models the self and inter-speaker influences of historical utterances with GRUs and hierarchical attention matching network. Moreover, the empathy phenomenon is also considered by an emotion state tracking component in SINN. Finally, the target utterance representation is enhanced by speaker influence aware context modeling, where the attention mechanism is used to extract the most relevant features for emotion classification. Experiment results on DailyDialog dataset confirm that our model consistently outperforms the state-of-the-art methods.