# SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection

arXiv: https://arxiv.org/abs/2503.07269 · HTML: https://mlsanity.com/paper/2503.07269/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 112 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Multilingual Language Models are not Multicultural: A Case Study in Emotion](https://mlsanity.com/paper/2307.01370/) (2023)
- [WASSA-2017 Shared Task on Emotion Intensity](https://mlsanity.com/paper/1708.03700/) (2017)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling](https://mlsanity.com/paper/1712.01741/) (2017)
- [No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages](https://mlsanity.com/paper/2411.03769/) (2024)
- [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024)
- [Does mBERT understand Romansh? Evaluating word embeddings using word alignment](https://mlsanity.com/paper/2306.08702/) (2023)
- [Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models](https://mlsanity.com/paper/2502.19856/) (2025)
- [Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification](https://mlsanity.com/paper/2502.19935/) (2025)

## Built on by (most cited first, 9 of 9)

- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages](https://mlsanity.com/paper/2510.23896/) (2025)
- [Exploring Cultural Nuances in Emotion Perception Across 15 African Languages](https://mlsanity.com/paper/2503.19642/) (2025)
- [EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian](https://mlsanity.com/paper/2505.23297/) (2025)
- [Beyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Tasks](https://mlsanity.com/paper/2605.09955/) (2026)
- [ArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2608.03358/) (2026)
- [VIBE: A VAD-Informed Benchmark for Entity-Centered Affective Profiling of Large Language Model Outputs](https://mlsanity.com/paper/2608.03810/) (2026)
- [Cross-lingual Functional Vectors for Emotion Detection in Large Language Models](https://mlsanity.com/paper/2608.29613/) (2026)
- [Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection](https://mlsanity.com/paper/2609.14570/) (2026)

## Abstract (arXiv)

We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource and are spoken across various continents. The data instances are multi-labeled with six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. Participants were asked to predict labels in three tracks: (a) multilabel emotion detection, (b) emotion intensity score detection, and (c) cross-lingual emotion detection. The task attracted over 700 participants. We received final submissions from more than 200 teams and 93 system description papers. We report baseline results, along with findings on the best-performing systems, the most common approaches, and the most effective methods across different tracks and languages. The datasets for this task are publicly available. The dataset is available at SemEval2025 Task 11 https://brighter-dataset.github.io