EmotionFlow: Capture the Dialogue Level Emotion Transitions

Emotion recognition in conversations (ERC) has attracted increasing interests in recent years, due to its wide range of applications, such as customer service analysis, health-care consultation, etc. One key challenge of ERC is that users' emotions would change due to the impact of others' emotions. That is, the emotions within the conversation can spread among the communication participants. However, the spread impact of emotions in a conversation is rarely addressed in existing researches. To this end, we propose \textbf{EmotionFlow} for ERC with the consideration of the spread of participants' emotions during a conversation. EmotionFlow first encodes users' utterance by concatenating the context with an auxiliary question, which helps to learn user-specific features. Then, conditional random field is applied to capture the sequential information at emotional level. We conduct extensive experiments on a public dataset Multimodal EmotionLines Dataset (MELD), and the results demonstrate the effectiveness of our proposed model.



Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Emotion Recognition in Conversation MELD EmotionFlow-large Weighted-F1 66.50 # 14
Emotion Recognition in Conversation MELD EmotionFlow-base Weighted-F1 65.05 # 29