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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 andspeaker-specific features, or integrating heterogeneous external commonsenseknowledge. Among them, some heavily rely on future contexts, which, however,are not always available in real-life scenarios. This fact inspires us togenerate pseudo future contexts to improve ERC. Specifically, for an utterance,we generate its future context with pre-trained language models, potentiallycontaining extra beneficial knowledge in a conversational form homogeneous withthe historical ones. These characteristics make pseudo future contexts easilyfused with historical contexts and historical speaker-specific contexts,yielding a conceptually simple framework systematically integratingmulti-contexts. Experimental results on four ERC datasets demonstrate ourmethod's superiority. Further in-depth analyses reveal that pseudo futurecontexts can rival real ones to some extent, especially in relativelycontext-independent conversations.

Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations