SAPBERT: Speaker-Aware Pretrained BERT for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) is receiving more and more attention, as interactions between humans and machines increase in a variety of services such as chat-bot and virtual assistants. As emotional expressions within a conversation can heavily depend on the contextual information of the participating speakers, it is important to capture self-dependency and inter-speaker dynamics. In this study, we propose a new pre-trained model, SAPBERT, that learns to identify speakers in a conversation to capture the speaker-dependent contexts and address the ERC task. SAPBERT is pre-trained with three training objectives including Speaker Classification (SC), Masked Utterance Regression (MUR), and Last Utterance Generation (LUG). We investigate whether our pre-trained speaker-aware model can be leveraged for capturing speaker-dependent contexts for ERC tasks. Experiments show that our proposed approach outperforms baseline models through demonstrating the effectiveness and validity of our method.