QuadAI在SemEval-2026任务3中的表现：混合RoBERTa与LLMs的集成学习用于维度方面基于情感分析
QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysis
AI总结：
QuadAI通过集成混合RoBERTa与LLMs的方法，在SemEval-2026任务3中实现了维度方面基于情感分析的高性能表现。
摘要：
我们提出了参与SemEval-2026任务3的系统，该系统结合了混合RoBERTa编码器和大语言模型（LLMs）通过预测层面的集成学习。混合编码器通过结合连续和离散化的情感表示来提高预测稳定性。我们进一步探索了通过上下文学习与LLMs以及岭回归堆叠来结合编码器和LLM的预测。在开发集上的实验结果表明，集成学习显著优于单独模型，实现了RMSE的大幅减少和相关性分数的提升。我们的发现展示了基于编码器和基于LLM方法在维度情感分析中的互补优势。我们的开发代码和资源将在https://github.com/aaronlifenghan/ABSentiment上共享。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
