@inproceedings{tangkulung-tsuruoka-2026-utokyo,
title = "{UT}okyo Tsuruoka Lab at {S}em{E}val-2026 Task 9: Efficient Single Forward Pass Inference for Multi-Label Polarization Classification",
author = "Tangkulung, Howard and
Tsuruoka, Yoshimasa",
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-1.211/",
pages = "1641--1651",
ISBN = "979-8-89176-414-9",
abstract = "Detecting and interpreting polarized online content is increasingly crucial as online platforms become central to public information exchange. We present an efficient adaptation of large language models for multi-label polarization classification in SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Our single-forward-pass inference method outperforms baseline multi-step decoding approaches for multi-label classification by reducing error propagation while improving inference efficiency. Beyond performance and efficiency analysis, we investigate the cross-lingual transferability of the system, observing statistically significant generalization within language families, a result that offers a practical path for low-resource language adaptation. Our system ranked 1st in 8 languages for Subtask 1 and 6 languages for Subtask 2, and placed in the top 5 for 16 out of 22 languages across both subtasks.Overall, we provide a simple, effective, and efficient solution for multilingual polarization classification."
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<abstract>Detecting and interpreting polarized online content is increasingly crucial as online platforms become central to public information exchange. We present an efficient adaptation of large language models for multi-label polarization classification in SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Our single-forward-pass inference method outperforms baseline multi-step decoding approaches for multi-label classification by reducing error propagation while improving inference efficiency. Beyond performance and efficiency analysis, we investigate the cross-lingual transferability of the system, observing statistically significant generalization within language families, a result that offers a practical path for low-resource language adaptation. Our system ranked 1st in 8 languages for Subtask 1 and 6 languages for Subtask 2, and placed in the top 5 for 16 out of 22 languages across both subtasks.Overall, we provide a simple, effective, and efficient solution for multilingual polarization classification.</abstract>
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%0 Conference Proceedings
%T UTokyo Tsuruoka Lab at SemEval-2026 Task 9: Efficient Single Forward Pass Inference for Multi-Label Polarization Classification
%A Tangkulung, Howard
%A Tsuruoka, Yoshimasa
%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 tangkulung-tsuruoka-2026-utokyo
%X Detecting and interpreting polarized online content is increasingly crucial as online platforms become central to public information exchange. We present an efficient adaptation of large language models for multi-label polarization classification in SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Our single-forward-pass inference method outperforms baseline multi-step decoding approaches for multi-label classification by reducing error propagation while improving inference efficiency. Beyond performance and efficiency analysis, we investigate the cross-lingual transferability of the system, observing statistically significant generalization within language families, a result that offers a practical path for low-resource language adaptation. Our system ranked 1st in 8 languages for Subtask 1 and 6 languages for Subtask 2, and placed in the top 5 for 16 out of 22 languages across both subtasks.Overall, we provide a simple, effective, and efficient solution for multilingual polarization classification.
%U https://aclanthology.org/2026.semeval-1.211/
%P 1641-1651
Markdown (Informal)
[UTokyo Tsuruoka Lab at SemEval-2026 Task 9: Efficient Single Forward Pass Inference for Multi-Label Polarization Classification](https://aclanthology.org/2026.semeval-1.211/) (Tangkulung & Tsuruoka, SemEval 2026)
ACL