# Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion

arXiv: https://arxiv.org/abs/2210.15842 · HTML: https://mlsanity.com/paper/2210.15842/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning](https://mlsanity.com/paper/1804.06658/) (2018)
- [Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification](https://mlsanity.com/paper/1802.00891/) (2018)
- [EmoGraph: Capturing Emotion Correlations using Graph Networks](https://mlsanity.com/paper/2008.09378/) (2020)
- [SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets](https://mlsanity.com/paper/1804.06137/) (2018)
- [Emotion Regulation and Dynamics of Moral Concerns During the Early COVID-19 Pandemic](https://mlsanity.com/paper/2203.03608/) (2022)

## Built on by (most cited first, 14 of 14)

- [Large Language Models Do Multi-Label Classification Differently](https://mlsanity.com/paper/2505.17510/) (2025)
- [The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition](https://mlsanity.com/paper/2403.17125/) (2024)
- [Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks](https://mlsanity.com/paper/2409.06173/) (2024)
- [Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats](https://mlsanity.com/paper/2211.00171/) (2022)
- [Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation](https://mlsanity.com/paper/2402.12862/) (2024)
- [Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts](https://mlsanity.com/paper/2505.17222/) (2025)
- [Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors](https://mlsanity.com/paper/2410.13776/) (2024)
- [Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit](https://mlsanity.com/paper/2212.00339/) (2022)
- [A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition](https://mlsanity.com/paper/2303.08027/) (2023)
- [Generating Fearful Images: Investigating Potential Emotional Biases in Image-Generation Models](https://mlsanity.com/paper/2411.05985/) (2024)
- [Posts of Peril: Detecting Information About Hazards in Text](https://mlsanity.com/paper/2405.17838/) (2024)
- [Semantic F1 Scores: Fair Evaluation Under Fuzzy Class Boundaries](https://mlsanity.com/paper/2509.21633/) (2025)
- [A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories](https://mlsanity.com/paper/2606.29068/) (2026)
- [Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter](https://mlsanity.com/paper/2607.02900/) (2026)

## Abstract (arXiv)

Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition models to improve emotion detection. First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM). Second, we integrate pairwise constraints of emotion representations as regularization terms alongside the classification loss of the models. We split these terms into two categories, local and global. The former dynamically change based on the gold labels, while the latter remain static during training. We demonstrate state-of-the-art performance across Spanish, English, and Arabic in SemEval 2018 Task 1 E-c using monolingual BERT-based models. On top of better performance, we also demonstrate improved robustness. Code is available at https://github.com/gchochla/Demux-MEmo.