Exploiting Pseudo Future Contexts for Emotion Recognition in
  Conversations
Yinyi Wei Shuaipeng Liu Hailei Yan Wei Ye Tong Mo Guanglu Wan
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.