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