用对比学习提升大模型情感识别能力，跨语言表现优异。
Chinchunmei at SemEval-2025 Task 11: Boosting the Large Language Model's Capability of Emotion Perception using Contrastive Learning
- 采用样本与生成双重对比学习，增强模型情感判断可靠性。
- 在英语多标签分类中获第9名，情感强度预测列第6名。
- 适合关注跨语言情感分析与大模型微调的研究者。
SemEval-2025 Task 11「弥合文本情感识别鸿沟」挑战覆盖28种语言，包含多标签分类（Track A）和情感强度预测（Track B）两个赛道，涵盖愤怒、恐惧、喜悦、悲伤、惊讶、厌恶六类情绪。本文系统探索两种对比学习方法：基于样本的对比学习（对比推理校准）与基于生成的对比学习（DPO、SimPO）。前者通过样本间对比提升预测可靠性，后者通过区分正确与错误生成来优化模型。所有模型均基于 LLaMa3-Instruct-8B 进行微调。系统在英语任务中获 Track A 第9名、Track B 第6名，其他语言亦进入顶尖水平。
原文摘要 · Abstract (English)
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.
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