  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.
