E xploiting U nsupervised D ata for E motion R ecognition in C onversations (2020)

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 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.

## introduction emotion recognition in conversations (erc) has garnered attention recently @xcite , due to its potential in developing practical chatting machines @xcite . unlike traditional text classification that handles context-free sentences, erc aims to predict the emotional state of each utterance in a conversation (figure 1 ). the inherent hierarchical structure of a conversation, i.e., wordsto-utterance and utterances-to-conversation, determines that the erc task should be better addressed by context-dependent models @xcite @xcite . in this paper, we aim to tackle the data scarcity issue of erc by exploiting the unsupervised data. specifically, we propose the conversation completion (convcom) task based on unsupervised conversation data, which attempts to select the correct answer from candidate answers to fill a masked utterance in a conversation. then, on the proposed convcom task, we pre-train a basic context-dependent encoder (pre-code). the hierarchical structure of the context-dependent encoder makes our work different from those that focus on universal sentence encoders @xcite @xcite . finally, we fine-tune the pre-code on five datasets of the erc task. experimental results show that the fine-tuned pre-code achieves significant improvement of performance over the baselines, particularly on minority emotion classes, demonstrating the effectiveness of our approach. question u1: they die at the end ? u2: oh, yes. u3: juliet poisons herself, then romeo comes in and he dies, too . u4: [ mask ] u5: ok. u6: but after that, their families are reconciled, so that's quite nice. u7: i don't understand why the montagons and the caplets just won't let them muck about together.