# The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation

arXiv: https://arxiv.org/abs/2206.07359 · HTML: https://mlsanity.com/paper/2206.07359/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [Shallow-to-Deep Training for Neural Machine Translation](https://mlsanity.com/paper/2010.03737/) (2020)
- [CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation](https://mlsanity.com/paper/2108.11626/) (2021)
- [Contrast and Generation Make BART a Good Dialogue Emotion Recognizer](https://mlsanity.com/paper/2112.11202/) (2021)
- [Label Confusion Learning to Enhance Text Classification Models](https://mlsanity.com/paper/2012.04987/) (2020)

## Built on by (most cited first, 4 of 4)

- [Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2403.20289/) (2024)
- [Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?](https://mlsanity.com/paper/2508.05474/) (2025)
- [EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation](https://mlsanity.com/paper/2607.17366/) (2026)
- [Multimodal Hidden Markov Models for Persistent Emotional State Tracking](https://mlsanity.com/paper/2605.12838/) (2026)

## Abstract (arXiv)

In emotion recognition in conversation (ERC), the emotion of the current utterance is predicted by considering the previous context, which can be utilized in many natural language processing tasks. Although multiple emotions can coexist in a given sentence, most previous approaches take the perspective of a classification task to predict only a given label. However, it is expensive and difficult to label the emotion of a sentence with confidence or multi-label. In this paper, we automatically construct a grayscale label considering the correlation between emotions and use it for learning. That is, instead of using a given label as a one-hot encoding, we construct a grayscale label by measuring scores for different emotions. We introduce several methods for constructing grayscale labels and confirm that each method improves the emotion recognition performance. Our method is simple, effective, and universally applicable to previous systems. The experiments show a significant improvement in the performance of baselines.