Academic

QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysis

arXiv:2603.07766v1 Announce Type: new Abstract: 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

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A. J. W. de Vink, Filippos Karolos Ventirozos, Natalia Amat-Lefort, Lifeng Han · · 1 min read · 14 views
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arXiv:2603.07766v1 Announce Type: new Abstract: 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

Executive Summary

The article presents a novel ensemble framework for dimensional aspect-based sentiment analysis at SemEval-2026 Task 3 by integrating a hybrid RoBERTa encoder with large language models (LLMs) via prediction-level ensemble learning. The hybrid encoder combines continuous and discretized sentiment representations, enhancing prediction stability, while the ensemble mechanism leverages both encoder-based and LLM-based strengths. Experimental results indicate significant performance gains—reduced RMSE and improved correlation scores—demonstrating the complementary efficacy of hybrid and LLM-based models. The authors open-source their code, facilitating reproducibility and community engagement.

Key Points

  • ▸ Hybrid RoBERTa encoder integrates regression and discretized classification heads
  • ▸ Prediction-level ensemble with LLMs improves performance metrics
  • ▸ Open-source availability supports reproducibility

Merits

Complementary Strength Integration

The combination of encoder-based and LLM-based approaches leverages the precision of encoder models with the contextual richness of LLMs, offering a robust solution for nuanced sentiment analysis.

Demerits

Complexity and Resource Burden

Ensemble architectures may introduce computational overhead and require more intensive training and inference resources, potentially limiting accessibility for smaller research teams or practitioners with constrained infrastructure.

Expert Commentary

This paper makes a substantive contribution to the field by effectively demonstrating the synergistic potential of hybrid encoder systems and LLMs in dimensional sentiment analysis. The integration of a hybrid encoder—capable of simultaneously capturing both continuous and discrete sentiment signals—represents a significant innovation, as it mitigates the binary reductionist tendency often seen in traditional sentiment modeling. Furthermore, the use of ridge-regression stacking to harmonize encoder and LLM outputs is a sophisticated methodological choice that enhances generalizability and reduces variance in predictions. While the computational cost of ensemble models remains a valid concern, the authors mitigate this by providing open access to code and resources, thereby enabling broader adoption and iterative improvement. Their work exemplifies a best-practice approach to model fusion in NLP: combining complementary strengths without compromising interpretability or scalability. Future work may explore dynamic weighting mechanisms in the ensemble to further optimize performance under varying data distributions.

Recommendations

  • ✓ Adopt hybrid encoder-LLM ensemble frameworks in dimensional sentiment analysis pipelines for enhanced accuracy
  • ✓ Explore dynamic ensemble weighting algorithms to improve adaptability across diverse datasets

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