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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

QuadAI's system for SemEval-2026 Task 3 addresses dimensional aspect-based sentiment regression by integrating a hybrid RoBERTa encoder with large language models (LLMs) through prediction-level ensemble learning. The RoBERTa encoder utilizes both regression and discretized classification heads, enhancing prediction stability by combining continuous and discrete sentiment representations. The system further incorporates in-context learning with LLMs and employs ridge-regression stacking to merge predictions from both the encoder and LLMs. Experimental results on the development set confirm that this ensemble learning strategy substantially improves performance, yielding significant reductions in RMSE and better correlation scores compared to individual models. This approach highlights the distinct yet complementary advantages of encoder-based and LLM-based methods for dimensional sentiment analysis.

Key takeaway

For NLP Engineers developing sentiment analysis systems, consider integrating hybrid encoder architectures with large language models using ensemble learning. This approach, demonstrated to reduce RMSE and improve correlation scores, offers a robust method for dimensional aspect-based sentiment regression. You should explore combining continuous and discretized sentiment representations and leverage techniques like ridge-regression stacking to maximize predictive stability and performance.

Key insights

Ensemble learning of hybrid RoBERTa and LLMs significantly improves dimensional aspect-based sentiment regression performance.

Principles

• Hybrid encoders boost prediction stability.
• Ensemble learning leverages complementary model strengths.
• Combining continuous and discrete representations is effective.

Method

The system combines a hybrid RoBERTa encoder (regression and classification heads) with LLMs using prediction-level ensemble learning, further employing in-context learning and ridge-regression stacking.

In practice

• Implement hybrid encoder with regression/classification heads.
• Utilize ridge-regression stacking for model fusion.
• Explore in-context learning with LLMs.

Topics

• SemEval-2026 Task 3
• Sentiment Analysis
• RoBERTa
• Large Language Models
• Ensemble Learning
• Dimensional Sentiment Regression
• Ridge Regression Stacking

Code references

• aaronlifenghan/ABSentiment

Best for: AI Engineer, Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.