The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations 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, Natural Language Processing · Depth: Expert, quick

Summary

The Classics team at SemEval-2026 Task 3 presents an approach for Dimensional Aspect-Based Sentiment Analysis, moving beyond traditional categorical sentiment to predict fine-grained, real-valued scores for "valence" (positivity) and "arousal" (intensity). Their method addresses two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full sentiment details including aspects, categories, opinions, and their scores (Subtask 3). For the regression task, their approach utilizes a weighted ensemble of transformer-based encoder models. Specifically for the Russian language, they enhance input with large language model (LLM)-generated synthetic sentiment descriptions. For the extraction task, a decoder LLM is fine-tuned to perform structured prediction, simultaneously identifying sentiment elements and estimating their numerical scores.

Key takeaway

For NLP Engineers developing advanced sentiment analysis systems, consider integrating transformer ensembles with LLM-generated annotations to achieve fine-grained dimensional sentiment predictions. If you are tackling low-resource languages like Russian, leveraging LLMs for synthetic data generation can significantly enhance model performance. This approach moves beyond simple positive/negative labels. It provides richer "valence" and "arousal" scores for specific aspects, also enabling simultaneous extraction of all sentiment details.

Key insights

Combining transformer ensembles and LLM-generated annotations enables fine-grained dimensional sentiment analysis for both regression and structured extraction tasks.

Method

For regression, use a weighted ensemble of transformer encoders, augmented with LLM-generated synthetic data for specific languages. For extraction, fine-tune a decoder LLM for structured prediction of sentiment elements and scores.

In practice

• Predict sentiment valence and arousal scores.
• Extract aspects, categories, opinions, and scores.
• Enhance low-resource language data with LLMs.

Topics

• Dimensional Sentiment Analysis
• Aspect-Based Sentiment Analysis
• Transformer Models
• Large Language Models
• Structured Prediction
• SemEval-2026 Task 3

Best for: 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.