Papers › EmotionFlow: Capture the Dialogue Level Emotion Transitions

EmotionFlow: Capture the Dialogue Level Emotion Transitions

7 May 2022ICASSP 2022 5archive 2025-07-28

Xiaohui Song, Liangjun Zang, Rong Zhang, Songlin Hu, Longtao Huang

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.

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Tasks

Emotion RecognitionEmotion Recognition in Conversation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Emotion Recognition in Conversation MELD EmotionFlow-large Weighted-F1 66.50 #20 of 68 Archive leaderboard report
Emotion Recognition in Conversation MELD EmotionFlow-base Weighted-F1 65.05 #35 of 68 Archive leaderboard report

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Methods

AdamAttentionAttention DropoutBERTCRFDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionRoBERTaSoftmaxWeight DecayWordPiece

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