Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs to overcome implicit contextual modelling of the original entailment style. Following the previous work, we further introduce the emotion information into graphs. Emotion information can markedly promote the detection of causal utterances whose emotion is the same as the targeted utterance. However, it is still hard to detect causal utterances with different emotions, especially neutral ones. The reason is that models are limited in reasoning causal clues and passing them between utterances. To alleviate this problem, we introduce social commonsense knowledge (CSK) and propose a Knowledge Enhanced Conversation graph (KEC). KEC propagates the CSK between two utterances. As not all CSK is emotionally suitable for utterances, we therefore propose a sentiment-realized knowledge selecting strategy to filter CSK. To process KEC, we further construct the Knowledge Enhanced Directed Acyclic Graph networks. Experimental results show that our method outperforms baselines and infers more causes with different emotions from the targeted utterance.
翻译:心电感应( Causal Causal Expecial competition) 旨在检测谈话中非中性目标语句的因果关系。 在这项工作中, 我们建立对话作为图表, 以克服原始导理风格的隐含背景建模。 在先前的工作之后, 我们进一步将情感信息引入图形。 情感信息可以明显促进因果言的检测, 其情感与目标语句相同。 但是, 仍然很难用不同的情感, 特别是中性情感来检测因果言。 原因是模型在推理因果线索和在言语之间传递这些线索方面受到限制。 为了缓解这一问题, 我们引入了社会常识知识( CSK), 并提出了知识增强对话图( KEC) 。 KEC 在两个语句之间传播CSK 。 由于并非所有 CSK 都在情感上适合表达语句, 因此我们提议一种感化知识选择战略来过滤 CSK。 为了处理 KEC, 我们进一步构建知识增强直接环形图网络。 实验结果显示, 我们的方法超越了基准, 并且推断出与有目标的情感不同。