The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2607.03414v1
• Date: Fri, 03 Jul 2026 15:22:46 GMT
• Title: The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis
• Abstract summary: This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis.<n>We investigate methods for moving beyond traditional categorical sentiment to predict fine-grained, real-valued scores for sentiment "valence" (positivity) and "arousal" (intensity)<n>For the Russian language, we further enhance the input by using a large language model (LLM) to generate synthetic sentiment descriptions.
• Score: 43.57029162591976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond traditional categorical sentiment (e.g., positive or negative) to predict fine-grained, real-valued scores for sentiment "valence" (positivity) and "arousal" (intensity). We participate in two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full sets of sentiment details, including aspects, categories, and opinions alongside their scores (Subtask 3). Our approach for the regression task involves a weighted ensemble of transformer-based encoder models. For the Russian language, we further enhance the input by using a large language model (LLM) to generate synthetic sentiment descriptions. For the extraction task, we fine-tune a decoder LLM to perform structured prediction, allowing the system to identify sentiment elements and estimate their numerical scores simultaneously.

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