Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment
This addresses causal emotion entailment in conversational AI, an incremental improvement for emotion analysis in dialogue systems.
The paper tackles the problem of identifying causal utterances responsible for emotions in conversations by proposing the Knowledge-Bridged Causal Interaction Network (KBCIN), which leverages commonsense knowledge as bridges to enhance understanding and reasoning, achieving better performance over most baseline models.
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.