A Joint Model with Contextual and Speaker Information for Conversational Causal Emotion Entailment

Conversational Causal Emotion Entailment (C2E2) aims to identify the causes of a target emotion in a non-neutral conversation. Most models treat C2E2 as an independent utterance pair classification problem that ignores the contextual information. Furthermore, most recent works focus only on the contribution of utterance information while ignoring the impact of speaker and emotional information. To solve these problems, we propose a joint model with the contextual and speaker information for conversational causal emotion entailment. We introduce the temporal convolutional network structure to effectively capture contextual information and help the model better understand and analyze emotional changes in conversations. At the same time, a multi-feature interaction network is proposed to use multiple features of utterance to analyze the causes of the emotion, the network extracts location information from the position-aware graph and uses speaker and emotion information to help the model understand the causes behind emotion generation. The experimental results demonstrate that our method outperforms the baseline method and can infer the causes of different emotions in more complex contexts.