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    <abstract>Aspect-based sentiment analysis has evolved from categorical polarity classification to fine-grained modeling of continuous affective dimensions. Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends this paradigm by requiring both structured sentiment extraction and continuous valence–arousal (VA) regression in multilingual settings. In this paper, we present our system for SemEval-2026 Task 3, which evaluates this challenge across six languages and four domains, requiring systems to extract aspect–category–opinion quadruplets and predict VA scores on a 1–9 scale.We propose a modular four-stage multilingual transformer pipeline for element extraction, aspect–opinion pairing, category prediction, and VA regression. We conduct experiments over multiple models and training configurations, including VA rescaling to [-1,1], Gaussian label noise injection, Concordance Correlation Coefficient (CCC) loss, and Savitzky–Golay smoothing. Among all languages, our system achieves the lowest RMSE of 0.5333 on Subtask 1 and the highest cF1 of 0.5492 on Subtask 2. We further investigate data augmentation to improve low-resource performance and address label imbalance. Ultimately, our modular architecture demonstrated highly competitive cross-lingual transfer, achieving top-tier placements in low-resource settings, including 2nd place for Tatar and 6th place for Russian in dimensional regression.</abstract>
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%0 Conference Proceedings
%T Habib University at SemEval-2026 Task 3: A Pipeline Approach for Dimensional Aspect-Based Sentiment Analysis
%A Affan, Muhammad
%A Shahzad, M. Hassan
%A Imam, Mikaal
%A Zulfiqar, Moiz
%A Kumar, Sandesh
%A Samad, Abdul
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F affan-etal-2026-habib
%X Aspect-based sentiment analysis has evolved from categorical polarity classification to fine-grained modeling of continuous affective dimensions. Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends this paradigm by requiring both structured sentiment extraction and continuous valence–arousal (VA) regression in multilingual settings. In this paper, we present our system for SemEval-2026 Task 3, which evaluates this challenge across six languages and four domains, requiring systems to extract aspect–category–opinion quadruplets and predict VA scores on a 1–9 scale.We propose a modular four-stage multilingual transformer pipeline for element extraction, aspect–opinion pairing, category prediction, and VA regression. We conduct experiments over multiple models and training configurations, including VA rescaling to [-1,1], Gaussian label noise injection, Concordance Correlation Coefficient (CCC) loss, and Savitzky–Golay smoothing. Among all languages, our system achieves the lowest RMSE of 0.5333 on Subtask 1 and the highest cF1 of 0.5492 on Subtask 2. We further investigate data augmentation to improve low-resource performance and address label imbalance. Ultimately, our modular architecture demonstrated highly competitive cross-lingual transfer, achieving top-tier placements in low-resource settings, including 2nd place for Tatar and 6th place for Russian in dimensional regression.
%U https://aclanthology.org/2026.semeval-1.428/
%P 3449-3459

Markdown (Informal)

[Habib University at SemEval-2026 Task 3: A Pipeline Approach for Dimensional Aspect-Based Sentiment Analysis](https://aclanthology.org/2026.semeval-1.428/) (Affan et al., SemEval 2026)

• Habib University at SemEval-2026 Task 3: A Pipeline Approach for Dimensional Aspect-Based Sentiment Analysis (Affan et al., SemEval 2026)

ACL

• Muhammad Affan, M Hassan Shahzad, Mikaal Imam, Moiz Zulfiqar, Sandesh Kumar, and Abdul Samad. 2026. Habib University at SemEval-2026 Task 3: A Pipeline Approach for Dimensional Aspect-Based Sentiment Analysis. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3449–3459, San Diego, California, USA. Association for Computational Linguistics.