# Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models

arXiv: https://arxiv.org/abs/2502.19856 · HTML: https://mlsanity.com/paper/2502.19856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding](https://mlsanity.com/paper/2412.17837/) (2024)
- [Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training](https://mlsanity.com/paper/2111.06181/) (2021)
- [TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection](https://mlsanity.com/paper/2405.17129/) (2024)
- [Cross-lingual Emotion Intensity Prediction](https://mlsanity.com/paper/2004.04103/) (2020)

## Built on by (most cited first, 1 of 1)

- [SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection](https://mlsanity.com/paper/2503.07269/) (2025)

## Abstract (arXiv)

This paper describes the system submitted by Team A to SemEval 2025 Task 11, ``Bridging the Gap in Text-Based Emotion Detection.'' The task involved identifying the perceived emotion of a speaker from text snippets, with each instance annotated with one of six emotions: joy, sadness, fear, anger, surprise, or disgust. A dataset provided by the task organizers served as the foundation for training and evaluating our models. Among the various approaches explored, the best performance was achieved using multilingual embeddings combined with a fully connected layer. This paper details the system architecture, discusses experimental results, and highlights the advantages of leveraging multilingual representations for robust emotion detection in text.