@inproceedings{adam-etal-2026-team-faisalm3at,
title = "Team faisalm3at {S}em{E}val-2026 Task 3: From Standard Regression to Distributional Alignment in Dimensional Sentiment Analysis",
author = "Adam, Faisal and
Aliyu, Lukman and
Aji, Sani and
Abubakar, Abdulhamid and
Shuaibu, Aliyu Rabiu",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.35/",
pages = "242--246",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes our participation in SemEval2026 Task 3: Dimensional Aspect-Based SentimentAnalysis (DimABSA) (Yu et al., 2026). We utilizeda pre-trained DeBERTa-V3 backbone to capturesemantic meaning through disentangled attention.While standard Mean Squared Error (MSE) loss establishes a performance floor, we propose a HybridMSE-CCCLoss to identify distributional relationships that simple regression missed. Our resultsdemonstrate a 54.6{\%} reduction in validation losscompared to the baseline, significantly improvingdetection in high-intensity emotional bins by mitigating the ``regression to the mean'' phenomenon."
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<abstract>This paper describes our participation in SemEval2026 Task 3: Dimensional Aspect-Based SentimentAnalysis (DimABSA) (Yu et al., 2026). We utilizeda pre-trained DeBERTa-V3 backbone to capturesemantic meaning through disentangled attention.While standard Mean Squared Error (MSE) loss establishes a performance floor, we propose a HybridMSE-CCCLoss to identify distributional relationships that simple regression missed. Our resultsdemonstrate a 54.6% reduction in validation losscompared to the baseline, significantly improvingdetection in high-intensity emotional bins by mitigating the “regression to the mean” phenomenon.</abstract>
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%0 Conference Proceedings
%T Team faisalm3at SemEval-2026 Task 3: From Standard Regression to Distributional Alignment in Dimensional Sentiment Analysis
%A Adam, Faisal
%A Aliyu, Lukman
%A Aji, Sani
%A Abubakar, Abdulhamid
%A Shuaibu, Aliyu Rabiu
%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 adam-etal-2026-team-faisalm3at
%X This paper describes our participation in SemEval2026 Task 3: Dimensional Aspect-Based SentimentAnalysis (DimABSA) (Yu et al., 2026). We utilizeda pre-trained DeBERTa-V3 backbone to capturesemantic meaning through disentangled attention.While standard Mean Squared Error (MSE) loss establishes a performance floor, we propose a HybridMSE-CCCLoss to identify distributional relationships that simple regression missed. Our resultsdemonstrate a 54.6% reduction in validation losscompared to the baseline, significantly improvingdetection in high-intensity emotional bins by mitigating the “regression to the mean” phenomenon.
%U https://aclanthology.org/2026.semeval-1.35/
%P 242-246
Markdown (Informal)
[Team faisalm3at SemEval-2026 Task 3: From Standard Regression to Distributional Alignment in Dimensional Sentiment Analysis](https://aclanthology.org/2026.semeval-1.35/) (Adam et al., SemEval 2026)
ACL