DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc. Currently, systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance... (read more)

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Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Emotion Recognition in Conversation IEMOCAP DialogueRNN F1 62.75 # 11
Accuracy 63.4 # 5
Emotion Recognition in Conversation SEMAINE DialogueRNN MAE (Valence) 0.168 # 2
MAE (Arousal) 0.165 # 2
MAE (Expectancy) 0.175 # 2
MAE (Power) 7.9 # 2

Methods used in the Paper


METHOD TYPE
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