  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 ConvCom 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.
