Skip to main content (https://arxiv.org/abs/2607.03414#content) arXiv is now an independent nonprofitLearn more× https://arxiv.org/IgnoreMehttps://arxiv.org/ Search arXiv Press Enter to search · Advanced search (https://arxiv.org/search/advanced) Computer Science > Computation and Language • *arXiv:2607.03414** (cs) [Submitted on 3 Jul 2026] Title:The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis Authors:Rafif Alshawi (https://arxiv.org/search/cs?searchtype=author&query=Alshawi,+R), Amit Raj (https://arxiv.org/search/cs?searchtype=author&query=Raj,+A), Aleksey Kudelya (https://arxiv.org/search/cs?searchtype=author&query=Kudelya,+A), Alexander Shirnin (https://arxiv.org/search/cs?searchtype=author&query=Shirnin,+A) View a PDF of the paper titled The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis, by Rafif Alshawi and 3 other authors View PDF (https://arxiv.org/pdf/2607.03414)HTML (experimental) (https://arxiv.org/html/2607.03414v1) 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. Subjects:Computation and Language (cs.CL) Cite as:arXiv:2607.03414 (https://arxiv.org/abs/2607.03414) [cs.CL] (or arXiv:2607.03414v1 (https://arxiv.org/abs/2607.03414v1) [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.03414 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Alexander Shirnin [view email (https://arxiv.org/show-email/7059a32e/2607.03414)] • *[v1]** Fri, 3 Jul 2026 15:22:46 UTC (52 KB) https://arxiv.org/abs/2607.03414Full-text links: Access Paper: View a PDF of the paper titled The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis, by Rafif Alshawi and 3 other authors • View PDF (https://arxiv.org/pdf/2607.03414) • HTML (experimental) (https://arxiv.org/html/2607.03414v1) • TeX Source (https://arxiv.org/src/2607.03414) view license (http://creativecommons.org/licenses/by/4.0/) Current browse context: cs.CL https://arxiv.org/prevnext?id=2607.03414&function=next&context=cs.CL new (https://arxiv.org/list/cs.CL/new) | recent (https://arxiv.org/list/cs.CL/recent) | 2026-07 (https://arxiv.org/list/cs.CL/2026-07) Change to browse by: cs (https://arxiv.org/abs/2607.03414?context=cs) References & Citations • NASA ADS (https://ui.adsabs.harvard.edu/abs/arXiv:2607.03414) • Google Scholar (https://scholar.google.com/scholar_lookup?arxiv_id=2607.03414) • Semantic Scholar (https://api.semanticscholar.org/arXiv:2607.03414) export BibTeX citation Loading... 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