Yan Wang author Jiayu Zhang author Jun Ma author Shaojun Wang author Jing Xiao author 2020-jul text Association for Computational Linguistics 1st virtual meeting conference publication 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-the-art methods on multiple emotion classification datasets. wang-etal-2020-contextualized https://www.aclweb.org/anthology/2020.sigdial-1.23 2020-jul 186 195