Exploiting unsupervised data for emotion recognition in conversations
Abstract
Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task.Unlike the sentence-level text classification problem, the available supervised data for the ERC task is limited, which potentially prevents the models from playing their maximum effect.In this paper, we propose a novel approach to leverage unsupervised conversation data, which is more accessible.Specifically, we propose the Conversation Completion (ConvCom) task, which attempts to select the correct answer from candidate answers to fill a masked utterance in a conversation.Then, we Pre-train a basic COntext-Dependent Encoder (PRE-CODE) on the Con-vCom task.Finally, we fine-tune the PRE-CODE on the datasets of ERC.Experimental results demonstrate that pre-training on unsupervised data achieves significant improvement of performance on the ERC datasets, particularly on the minority emotion classes. 1
Authors: Wenxiang Jiao, Michael Rung-Tsong Lyu, Irwin King