Contextualized Emotion Recognition in Conversation as Sequence Tagging
Wang Yan, Jiayu Zhang, Jun Ma, Shaojun Wang, Jing Zhong Xiao · 2020
Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on.In this paper, we propose a method to model ERC task as sequence tagging where a Conditional Random Field (CRF) layer is leveraged to learn the emotional consistency in the conversation.We employ LSTM-based encoders that capture self and inter-speaker dependency of interlocutors to generate contextualized utterance representations which are fed into the CRF layer.For capturing long-range global context, we use a multi-layer Transformer encoder to enhance the LSTM-based encoder.Experiments show that our method benefits from modeling the emotional consistency and outperforms the current state-of-theart methods on multiple emotion classification datasets.