@inproceedings{lin-etal-2026-team,
title = "Team {YTY} at {S}em{E}val 2026 task 12: Option-Aware Retrieval and Cross-Encoder Reasoning Framework for Abductive Event Reasoning",
author = "Lin, Junxin and
Meng, Zhichao and
Jiang, Lianxin",
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-1.249/",
pages = "1979--1987",
ISBN = "979-8-89176-414-9",
abstract = "We describe a unified system for SemEval-2026 Task 9 on multilingual polarization detection. The task requires binary polarization detection, multi-label target type classification, and multi-label manifestation identification across languages and events with severe class imbalance. Our approach combines (i) targeted data augmentation for low-frequency labels, (ii) merged multitask fine-tuning of Subtask 2 and Subtask 3, and (iii) model fusion to improve cross-lingual stability. Subtask 1 predictions are derived via calibrated inference from the multi-label head. On the development set, multitask training consistently out-performs single-task variants, and fusion yields additional gains, especially for rare labels. We also report ablations and error analyses, highlighting remaining challenges such as implicit polarization and partial-label uncertainty."
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<abstract>We describe a unified system for SemEval-2026 Task 9 on multilingual polarization detection. The task requires binary polarization detection, multi-label target type classification, and multi-label manifestation identification across languages and events with severe class imbalance. Our approach combines (i) targeted data augmentation for low-frequency labels, (ii) merged multitask fine-tuning of Subtask 2 and Subtask 3, and (iii) model fusion to improve cross-lingual stability. Subtask 1 predictions are derived via calibrated inference from the multi-label head. On the development set, multitask training consistently out-performs single-task variants, and fusion yields additional gains, especially for rare labels. We also report ablations and error analyses, highlighting remaining challenges such as implicit polarization and partial-label uncertainty.</abstract>
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%0 Conference Proceedings
%T Team YTY at SemEval 2026 task 12: Option-Aware Retrieval and Cross-Encoder Reasoning Framework for Abductive Event Reasoning
%A Lin, Junxin
%A Meng, Zhichao
%A Jiang, Lianxin
%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 lin-etal-2026-team
%X We describe a unified system for SemEval-2026 Task 9 on multilingual polarization detection. The task requires binary polarization detection, multi-label target type classification, and multi-label manifestation identification across languages and events with severe class imbalance. Our approach combines (i) targeted data augmentation for low-frequency labels, (ii) merged multitask fine-tuning of Subtask 2 and Subtask 3, and (iii) model fusion to improve cross-lingual stability. Subtask 1 predictions are derived via calibrated inference from the multi-label head. On the development set, multitask training consistently out-performs single-task variants, and fusion yields additional gains, especially for rare labels. We also report ablations and error analyses, highlighting remaining challenges such as implicit polarization and partial-label uncertainty.
%U https://aclanthology.org/2026.semeval-1.249/
%P 1979-1987
Markdown (Informal)
[Team YTY at SemEval 2026 task 12: Option-Aware Retrieval and Cross-Encoder Reasoning Framework for Abductive Event Reasoning](https://aclanthology.org/2026.semeval-1.249/) (Lin et al., SemEval 2026)
ACL