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Conversational emotion recognition (CER) has attracted increasing interestsin the natural language processing (NLP) community. Different from the vanillaemotion recognition, effective speaker-sensitive utterance representation isone major challenge for CER. In this paper, we exploit speaker identification(SI) as an auxiliary task to enhance the utterance representation inconversations. By this method, we can learn better speaker-aware contextualrepresentations from the additional SI corpus. Experiments on two benchmarkdatasets demonstrate that the proposed architecture is highly effective forCER, obtaining new state-of-the-art results on two datasets.

Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition