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

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

URL: http://arxiv.org/abs/2507.15714v1
Date: Mon, 21 Jul 2025 15:25:47 GMT
Title: Chinchunmei at SemEval-2025 Task 11: Boosting the Large Language Model's Capability of Emotion Perception using Contrastive Learning
Authors: Tian Li, Yujian Sun, Huizhi Liang,
Abstract summary: SemEval-2025 Task 11, Bridging the Gap in Text-Based Emotion Detection, introduces an emotion recognition challenge spanning over 28 languages.<n>This competition encourages researchers to explore more advanced approaches to address the challenges posed by the diversity of emotional expressions and background variations.<n>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.
Score: 7.7913577883363425
License: http://creativecommons.org/licenses/by/4.0/
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.

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