Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2306.15376v1
• Date: Tue, 27 Jun 2023 10:51:02 GMT
• Title: Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations
• Authors: Yinyi Wei, Shuaipeng Liu, Hailei Yan, Wei Ye, Tong Mo, Guanglu Wan
• Abstract summary: We generate pseudo future contexts to improve emotion recognition in conversations. For an utterance, we generate its future context with pre-trained language models. These characteristics make pseudo future contexts easily fused with historical contexts and historical speaker-specific contexts.
• Score: 3.3961757428667925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the extensive accumulation of conversational data on the Internet, emotion recognition in conversations (ERC) has received increasing attention. Previous efforts of this task mainly focus on leveraging contextual and speaker-specific features, or integrating heterogeneous external commonsense knowledge. Among them, some heavily rely on future contexts, which, however, are not always available in real-life scenarios. This fact inspires us to generate pseudo future contexts to improve ERC. Specifically, for an utterance, we generate its future context with pre-trained language models, potentially containing extra beneficial knowledge in a conversational form homogeneous with the historical ones. These characteristics make pseudo future contexts easily fused with historical contexts and historical speaker-specific contexts, yielding a conceptually simple framework systematically integrating multi-contexts. Experimental results on four ERC datasets demonstrate our method's superiority. Further in-depth analyses reveal that pseudo future contexts can rival real ones to some extent, especially in relatively context-independent conversations.

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