Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge (2023)

Abstract: Emotion Cause Triplet Extraction in Conversations (ECTEC) aims to simultaneously extract emotion utterances, emotion categories, and cause utterances from conversations. However, existing studies mainly decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner. Moreover, since conversations tend to contain many informal and implicit expressions, it often requires external knowledge and reasoning-based inference to accurately identify emotional and causal clues implicitly mentioned in the context, which are ignored by previous work. To address these limitations, in this paper, we propose a commonSense knowledge-enHanced generAtive fRameworK named SHARK, which formulates the ECTEC task as an index generation problem and generates the emotion-cause-category triplets in an end-to-end manner with a sequence-to-sequence model. Furthermore, we propose to incorporate both retrieved and generated commonsense knowledge into the generative model via a dual-view gate mechanism and a graph attention layer. Experimental results show that our SHARK model consistently outperforms several competitive systems on two benchmark datasets. Our source codes are publicly released at https://github.com/NUSTM/SHARK.

## introduction emotion understanding is a key component of human-like artificial intelligence, as emotions are intrinsic to humans and significantly influence our cognition, decision-making, and social interactions. conversations, as a fundamental form of human communication, are replete with diverse emotions. beyond mere emotion recognition, delving into the triggers behind these emotions in conversations is a more intricate and less explored task. a comprehensive understanding of both the speaker's emotions and their causes facilitates many applications such as customer support, mental health care, and human-computer interaction. therefore, emotion cause analysis in conversations (ecac) has been gaining increasing attention from both academia and industry in recent years. ## conclusion in this paper, we proposed a commonsense knowledge-enhanced generative framework named shark for the emotion cause triplet extraction in conversations (ectec) task. specifically, we formulated the ectec task as an index generation problem and employed a bart-based model to generate all the emotion-cause-category triplets in one shot. moreover, we designed a dualview gate mechanism and a graph attention layer to incorporate both the retrieved and generated commonsense knowledge. experimental results on two benchmark datasets show the superiority of shark over a number of comparison systems and the usefulness of commonsense knowledge.