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.

翻译：先前的研究大多侧重于以顺序编码模拟对话背景,忽视了发言和谈话特点之间的信息互动。在本文中,我们调查了谈话结构对于处理发言互动和谈话特点的重要性。为此,我们提议了一个对这项任务的谈话觉悟模式(DAM)。具体地说,我们共同模拟ECECC, 其讨论结构使用多任务学习框架进行区分,并通过门形神经网络(GNN)明确编码谈话结构,将丰富的语音互动信息纳入我们的模型。此外,我们利用GNNN, 进一步加强我们的ECECC模式, 其具体对话特点。基准材料的结果表明, DAM 超越了文献中的状态(SOTA)系统。 这表明, 谈话结构可能包含情感表达与相应原因表达之间的潜在联系。 它还核查了谈话特征的有效性。 该文件的代码将可在 Giubb上查阅。