No abstract

The expression of emotions is a crucial part of daily human communication. Modeling the conversational and sequential context has seen much success and plays a vital role in Emotion Recognition in Conversations (ERC). However, existing approaches either model only one of the two or employ naive late-fusion methodologies to obtain final utterance representations. This paper proposes a novel idea to incorporate both these contexts and better model the intrinsic structure within a conversation. More precisely, we propose a novel architecture boosted by a modified LSTM cell, which we call DiscLSTM, that better captures the interaction between conversational and sequential context. DiscLSTM brings together the best of both worlds and provides a more intuitive and efficient way to model the information flow between individual utterances by better capturing long-distance conversational background through discourse relations and sequential context through recurrence. We conduct experiments on four benchmark datasets for ERC and show that our model achieves performance competitive to state-of-the-art and at times performs better than other graph-based approaches in literature, with a conversational graph that is both sparse and avoids complicated edge relations like much of previous work. We make all our codes publicly available on GitHub 1 .