Chinchunmei at SemEval-2025 Task 11: Boosting the Large Language Model's Capability of Emotion Perception using Contrastive Learning

TL;DR

This paper presents a system that enhances large language models' ability to perceive emotions across multiple languages by employing contrastive learning techniques, achieving competitive results in SemEval-2025 Task 11.

Contribution

It introduces two novel contrastive learning methods—sample-based and generation-based—for improving emotion recognition in multilingual LLMs.

Findings

Achieved top-tier performance in multilingual emotion detection

Demonstrated effectiveness of contrastive learning approaches

Ranked 9th in emotion classification and 6th in intensity prediction for English

Abstract

The SemEval-2025 Task 11, Bridging the Gap in Text-Based Emotion Detection, introduces an emotion recognition challenge spanning over 28 languages. This competition encourages researchers to explore more advanced approaches to address the challenges posed by the diversity of emotional expressions and background variations. It features two tracks: multi-label classification (Track A) and emotion intensity prediction (Track B), covering six emotion categories: anger, fear, joy, sadness, surprise, and disgust. In our work, we systematically explore the benefits of two contrastive learning approaches: sample-based (Contrastive Reasoning Calibration) and generation-based (DPO, SimPO) contrastive learning. The sample-based contrastive approach trains the model by comparing two samples to generate more reliable predictions. The generation-based contrastive approach trains the model to differentiate between correct and incorrect generations, refining its prediction. All models are fine-tuned from LLaMa3-Instruct-8B. Our system achieves 9th place in Track A and 6th place in Track B for English, while ranking among the top-tier performing systems for other languages.