提出统一框架，高效完成多语言情感分析的三类任务。
AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysis
- 用微调编码器+语言特化LoRA指令调优，实现参数高效适配。
- 在多数评估场景中超越基线，保持强性能且降低训练开销。
- 适合需要跨语言、多领域情感分析的工业级应用开发者。
本文介绍AILS-NTUA系统在SemEval-2026任务3（维度层面的情感分析，DimABSA）Track-A中的表现，涵盖三项互补任务：维度层面情感回归（DimASR）、维度层面情感三元组抽取（DimASTE）与维度层面情感四元组预测（DimASQP），均在多语言、多领域框架下完成。方法结合语言适配编码器的微调以实现连续层面情感预测，并通过针对不同语言的大型语言模型进行基于LoRA的指令调优，完成结构化三元组与四元组抽取。该统一但任务自适应的设计强调跨语言与领域的参数高效专业化，实现更低的训练与推理成本，同时保持优异性能。实证结果表明，所提模型在多数评估设置中表现竞争性，且持续优于提供基线。
原文摘要 · Abstract (English)
In this paper, we present AILS-NTUA system for Track-A of SemEval-2026 Task 3 on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which encompasses three complementary problems: Dimensional Aspect Sentiment Regression (DimASR), Dimensional Aspect Sentiment Triplet Extraction (DimASTE), and Dimensional Aspect Sentiment Quadruplet Prediction (DimASQP) within a multilingual and multi-domain framework. Our methodology combines fine-tuning of language-appropriate encoder backbones for continuous aspect-level sentiment prediction with language-specific instruction tuning of large language models using LoRA for structured triplet and quadruplet extraction. This unified yet task-adaptive design emphasizes parameter-efficient specialization across languages and domains, enabling reduced training and inference requirements while maintaining strong effectiveness. Empirical results demonstrate that the proposed models achieve competitive performance and consistently surpass the provided baselines across most evaluation settings.
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