用大模型生成情感描述，提升细粒度情感分析精度
The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis
- 融合Transformer与大模型生成的标注数据，提升情感评分预测能力
- 在俄语数据上，大模型生成的描述使性能提升12.3个点
- 支持同时抽取情感要素与数值评分，适合多维度情感分析任务
本文针对SemEval-2026任务3：维度化方面情感分析，提出一种细粒度情感分析方法。传统情感分类仅输出正/负等类别，而本工作致力于预测情感的'效价'（正向程度）和'唤醒度'（强度）等连续值。参与两个子任务：为指定方面预测连续情感分（子任务1），以及提取完整的感情信息（包括方面、类别、观点及对应分数，子任务3）。回归任务采用基于Transformer编码器的加权集成模型；对俄语数据，进一步利用大语言模型（LLM）生成合成情感描述以增强输入。抽取任务则微调解码器型大模型，实现结构化预测，可同步识别情感元素并估计其数值分数。
原文摘要 · Abstract (English)
This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond traditional categorical sentiment (e.g., positive or negative) to predict fine-grained, real-valued scores for sentiment "valence" (positivity) and "arousal" (intensity). We participate in two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full sets of sentiment details, including aspects, categories, and opinions alongside their scores (Subtask 3). Our approach for the regression task involves a weighted ensemble of transformer-based encoder models. For the Russian language, we further enhance the input by using a large language model (LLM) to generate synthetic sentiment descriptions. For the extraction task, we fine-tune a decoder LLM to perform structured prediction, allowing the system to identify sentiment elements and estimate their numerical scores simultaneously.
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