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.

翻译：随着互联网上对话数据的大量积累，对话情感识别（ERC）受到越来越多的关注。以往该任务的研究主要集中于利用上下文和说话者特定特征，或整合异构的外部常识知识。其中，一些方法严重依赖未来上下文，然而在现实场景中这些上下文并非总是可用。这一事实启发我们生成伪未来上下文以改进ERC。具体而言，针对某句话，我们利用预训练语言模型生成其未来上下文，这些上下文可能蕴含额外的有用知识，且与历史上下文具有同质化的对话形式。这些特性使得伪未来上下文易于与历史上下文及历史说话者特定上下文融合，从而形成一个概念上简单、系统整合多上下文的框架。在四个ERC数据集上的实验结果表明了我们方法的优越性。进一步的深入分析显示，伪未来上下文在一定程度上可与真实上下文相媲美，尤其是在相对上下文无关的对话中。