@inproceedings{dao-etal-2026-puerai,
title = "{P}uer{AI} at {S}em{E}val-2026 Task 5: Homograph Appropriateness Assessment via {D}e{BERT}a Contrastive Regression and Contextual Grouping",
author = "Dao, Jiaxu and
Li, Zhuoying and
Ma, Hangchao and
Tong, Jinli and
Lan, Xiaoli and
Lu, Yifan and
Yang, Zhanji",
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-1.41/",
pages = "284--289",
ISBN = "979-8-89176-414-9",
abstract = "To assess homograph appropriateness in narrative contexts for SemEval-2026 Task 5, we propose a contrastive regression framework. This approach combines candidate sense definitions with full narrative texts to establish an MSE regression baseline, further enhanced by a contextual grouping ranking loss that models relative rationality among senses. Evaluated on the official AmbiStory dataset, our method consistently outperforms the baseline in accuracy and Spearman correlation. These results validate the efficacy of relative order modeling for capturing fine-grained semantic nuances in complex narratives. The code is available at: https://github.com/daojiaxu/Semeval2026task5."
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<abstract>To assess homograph appropriateness in narrative contexts for SemEval-2026 Task 5, we propose a contrastive regression framework. This approach combines candidate sense definitions with full narrative texts to establish an MSE regression baseline, further enhanced by a contextual grouping ranking loss that models relative rationality among senses. Evaluated on the official AmbiStory dataset, our method consistently outperforms the baseline in accuracy and Spearman correlation. These results validate the efficacy of relative order modeling for capturing fine-grained semantic nuances in complex narratives. The code is available at: https://github.com/daojiaxu/Semeval2026task5.</abstract>
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%0 Conference Proceedings
%T PuerAI at SemEval-2026 Task 5: Homograph Appropriateness Assessment via DeBERTa Contrastive Regression and Contextual Grouping
%A Dao, Jiaxu
%A Li, Zhuoying
%A Ma, Hangchao
%A Tong, Jinli
%A Lan, Xiaoli
%A Lu, Yifan
%A Yang, Zhanji
%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 dao-etal-2026-puerai
%X To assess homograph appropriateness in narrative contexts for SemEval-2026 Task 5, we propose a contrastive regression framework. This approach combines candidate sense definitions with full narrative texts to establish an MSE regression baseline, further enhanced by a contextual grouping ranking loss that models relative rationality among senses. Evaluated on the official AmbiStory dataset, our method consistently outperforms the baseline in accuracy and Spearman correlation. These results validate the efficacy of relative order modeling for capturing fine-grained semantic nuances in complex narratives. The code is available at: https://github.com/daojiaxu/Semeval2026task5.
%U https://aclanthology.org/2026.semeval-1.41/
%P 284-289
Markdown (Informal)
[PuerAI at SemEval-2026 Task 5: Homograph Appropriateness Assessment via DeBERTa Contrastive Regression and Contextual Grouping](https://aclanthology.org/2026.semeval-1.41/) (Dao et al., SemEval 2026)
ACL
- Jiaxu Dao, Zhuoying Li, Hangchao Ma, Jinli Tong, Xiaoli Lan, Yifan Lu, and Zhanji Yang. 2026. PuerAI at SemEval-2026 Task 5: Homograph Appropriateness Assessment via DeBERTa Contrastive Regression and Contextual Grouping. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 284–289, San Diego, California, USA. Association for Computational Linguistics.