论文标题
知识桥梁的因果关系网络，用于因果情绪
Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment
论文作者
论文摘要
因果情绪的旨在确定对话中非中性情绪导致目标话语的因果话语。以前的作品在彻底了解对话环境和情感原因的准确推理方面受到限制。为此，我们提出了以常识性知识（CSK）为三个桥梁的知识桥梁因果相互作用网络（KBCIN）。具体而言，我们为每个对话构建了一个对话图，并利用以事件为中心的CSK作为语义级桥（S-Bridge），通过CSK增强图形注意模块在对话上下文中捕获深层的插座依赖性。此外，社交互动CSK充当情感级别的桥梁（E-Bridge）和动作级别的桥梁（A-Bridge），将候选人的话语与目标一般连接起来，这为情感交互模块和动作互动模块提供了明确的因果线索，以推理目标情绪。实验结果表明，我们的模型在大多数基线模型中都能达到更好的性能。我们的源代码可在https://github.com/circle-hit/kbcin上公开获得。
Causal Emotion Entailment aims to identify causal utterances that are responsible for the target utterance with a non-neutral emotion in conversations. Previous works are limited in thorough understanding of the conversational context and accurate reasoning of the emotion cause. To this end, we propose Knowledge-Bridged Causal Interaction Network (KBCIN) with commonsense knowledge (CSK) leveraged as three bridges. Specifically, we construct a conversational graph for each conversation and leverage the event-centered CSK as the semantics-level bridge (S-bridge) to capture the deep inter-utterance dependencies in the conversational context via the CSK-Enhanced Graph Attention module. Moreover, social-interaction CSK serves as emotion-level bridge (E-bridge) and action-level bridge (A-bridge) to connect candidate utterances with the target one, which provides explicit causal clues for the Emotional Interaction module and Actional Interaction module to reason the target emotion. Experimental results show that our model achieves better performance over most baseline models. Our source code is publicly available at https://github.com/circle-hit/KBCIN.
