  Emotion Cause Extraction in Conversations (ECEC) aims to extract the
utterances which contain the emotional cause in conversations. Most prior
research focuses on modelling conversational contexts with sequential encoding,
ignoring the informative interactions between utterances and
conversational-specific features for ECEC. In this paper, we investigate the
importance of discourse structures in handling utterance interactions and
conversationspecific features for ECEC. To this end, we propose a
discourse-aware model (DAM) for this task. Concretely, we jointly model ECEC
with discourse parsing using a multi-task learning (MTL) framework and
explicitly encode discourse structures via gated graph neural network (gated
GNN), integrating rich utterance interaction information to our model. In
addition, we use gated GNN to further enhance our ECEC model with
conversation-specific features. Results on the benchmark corpus show that DAM
outperform the state-of-theart (SOTA) systems in the literature. This suggests
that the discourse structure may contain a potential link between emotional
utterances and their corresponding cause expressions. It also verifies the
effectiveness of conversationalspecific features. The codes of this paper will
be available on GitHub.

Computation and Language