融合罗伯特与大模型，提升情感分析精度。
QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysis
- 用混合编码器同时做连续与离散情感预测，增强稳定性。
- 集成学习使测试集RMSE大幅降低，相关性显著提升。
- 适合需要高精度情感分析的研究者和工业应用。
我们为 SemEval-2026 任务 3 提出的维度化方面情感回归系统，结合了混合 RoBERTa 编码器与大语言模型（LLMs）的预测级集成学习。混合编码器通过联合回归与离散分类头预测情感，利用连续与离散表示的互补性提升预测稳定性。进一步采用上下文学习与岭回归堆叠策略，融合编码器与 LLM 的预测结果。在开发集上的实验表明，集成学习显著优于单个模型，实现 RMSE 显著降低与相关性评分提升。结果验证了编码器与大模型在维度情感分析中的互补优势。开发代码与资源将公开于 https://github.com/aaronlifenghan/ABSentiment。
原文摘要 · Abstract (English)
We present our system for SemEval-2026 Task 3 on dimensional aspect-based sentiment regression. Our approach combines a hybrid RoBERTa encoder, which jointly predicts sentiment using regression and discretized classification heads, with large language models (LLMs) via prediction-level ensemble learning. The hybrid encoder improves prediction stability by combining continuous and discretized sentiment representations. We further explore in-context learning with LLMs and ridge-regression stacking to combine encoder and LLM predictions. Experimental results on the development set show that ensemble learning significantly improves performance over individual models, achieving substantial reductions in RMSE and improvements in correlation scores. Our findings demonstrate the complementary strengths of encoder-based and LLM-based approaches for dimensional sentiment analysis. Our development code and resources will be shared at https://github.com/aaronlifenghan/ABSentiment
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