# Exploiting Unsupervised Data for Emotion Recognition in Conversations

arXiv: https://arxiv.org/abs/2010.01908 · HTML: https://mlsanity.com/paper/2010.01908/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://mlsanity.com/paper/2306.01505/) (2023)
- [Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation](https://mlsanity.com/paper/2106.00941/) (2021)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation](https://mlsanity.com/paper/2203.08442/) (2022)

## Abstract (arXiv)

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.