Enhancing emotion inference in conversations with commonsense knowledge

Existing studies on emotion analysis in conversations have mainly focused on recognizing the emotion of a given utterance. This paper investigates the task of emotion inference in conversations, which explores how the utterances affect the addressee’s emotion, without knowing the addressee’s response yet. While it is straightforward for humans to perceive and reason about the feelings of others in conversations, it is a severe challenge for machines, mainly due to the lack of commonsense knowledge. In this work, we propose to leverage external inferential knowledge to enhance the emotion inference in conversations. Specifically, a conversation modeling module is designed to accumulate information from the conversation history based on the emotional interaction between the addressee and writers. In addition, a knowledge integration strategy is also proposed to integrate the conversation-related commonsense knowledge generated from the event-based knowledge graph. The experiments on three different benchmark conversational datasets demonstrate the effectiveness of the proposed models, and prove the benefits of commonsense knowledge for emotion inference in conversations.