用XLM-RoBERTa微调预测多语言情感的连续维度得分
NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression
- 基于XLM-RoBERTa构建双回归头，输出缩放后的情绪维度值
- 在英中双语、三领域数据上均达到[1,9]区间内高精度预测
- 微调效果优于大模型提示方法，尤其适合小样本场景
维度化方面感知情感分析（DimABSA）将传统分类极性扩展为连续的效价-唤醒度（VA）回归任务。本文介绍针对SemEval-2026任务3赛道A子任务1（维度化方面情感回归）的系统设计，目标是为文本中每个方面预测[1,9]范围内的真实值效价与唤醒度分数。采用基于XLM-RoBERTa-base的微调方法，配置双回归头并使用Sigmoid输出缩放，分别处理效价与唤醒度预测。针对英文和中文在餐厅、笔记本电脑、金融三个领域的组合，训练独立模型，并合并训练与开发集进行最终测试预测。开发实验表明，在少样本提示设置下，该微调方法优于多个大型语言模型，跨所有评估数据集表现更优。
原文摘要 · Abstract (English)
Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regression. This paper describes a system developed for Track A, Subtask 1 (Dimensional Aspect Sentiment Regression), aiming to predict real-valued VA scores in the [1, 9] range for each given aspect in a text. A fine-tuning approach based on XLM-RoBERTa-base is adopted, with dual regression heads with sigmoid-scaled outputs for valence and arousal prediction. Separate models are trained for each language-domain pair (English and Chinese across restaurant, laptop, and finance domains), and training and development sets are merged for final test predictions. In development experiments, the fine-tuning approach is compared against several large language models under a few-shot prompting setting, demonstrating that task-specific fine-tuning outperforms these LLM-based methods across all evaluation datasets.
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