@inproceedings{akrap-etal-2026-fer,
title = "{FER} at {S}em{E}val-2026 Task 6: Analysis of Different Approaches to Unmasking Political Question Evasions",
author = "Akrap, Matija and
Bili{\'c}, Andrija and
{\v{S}}impraga, Roko and
Ra{\v{c}}i{\'c}, Fran and
{\v{C}}uturilo, Luka",
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-1.5/",
pages = "30--36",
ISBN = "979-8-89176-414-9",
abstract = "We tackle classifying evasive political answerswithin the context of SemEval-2026 Task 6 andcompare three modeling strategies: a flat base-line, a hierarchical cascade, and a multitasklearning approach. Our experiments demon-strate that a hierarchical RoBERTa-base modelachieves the best performance, particularly byleveraging the distinctiveness of the class ClearNon-Reply. Conversely, we find that stan-dard multitask learning frequently producesstructurally invalid label combinations in a sig-nificant fraction of predictions. Our demon-strations show that applying a constrained in-ference mask eliminates these errors entirelywhile improving F1 performance, whereas afully joint training approach underperforms dueto data sparsity. Finally, we employ datasetcartography to compare training dynamics be-tween the hierarchical and multitask approach."
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<abstract>We tackle classifying evasive political answerswithin the context of SemEval-2026 Task 6 andcompare three modeling strategies: a flat base-line, a hierarchical cascade, and a multitasklearning approach. Our experiments demon-strate that a hierarchical RoBERTa-base modelachieves the best performance, particularly byleveraging the distinctiveness of the class ClearNon-Reply. Conversely, we find that stan-dard multitask learning frequently producesstructurally invalid label combinations in a sig-nificant fraction of predictions. Our demon-strations show that applying a constrained in-ference mask eliminates these errors entirelywhile improving F1 performance, whereas afully joint training approach underperforms dueto data sparsity. Finally, we employ datasetcartography to compare training dynamics be-tween the hierarchical and multitask approach.</abstract>
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%0 Conference Proceedings
%T FER at SemEval-2026 Task 6: Analysis of Different Approaches to Unmasking Political Question Evasions
%A Akrap, Matija
%A Bilić, Andrija
%A Šimpraga, Roko
%A Račić, Fran
%A Čuturilo, Luka
%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 akrap-etal-2026-fer
%X We tackle classifying evasive political answerswithin the context of SemEval-2026 Task 6 andcompare three modeling strategies: a flat base-line, a hierarchical cascade, and a multitasklearning approach. Our experiments demon-strate that a hierarchical RoBERTa-base modelachieves the best performance, particularly byleveraging the distinctiveness of the class ClearNon-Reply. Conversely, we find that stan-dard multitask learning frequently producesstructurally invalid label combinations in a sig-nificant fraction of predictions. Our demon-strations show that applying a constrained in-ference mask eliminates these errors entirelywhile improving F1 performance, whereas afully joint training approach underperforms dueto data sparsity. Finally, we employ datasetcartography to compare training dynamics be-tween the hierarchical and multitask approach.
%U https://aclanthology.org/2026.semeval-1.5/
%P 30-36
Markdown (Informal)
[FER at SemEval-2026 Task 6: Analysis of Different Approaches to Unmasking Political Question Evasions](https://aclanthology.org/2026.semeval-1.5/) (Akrap et al., SemEval 2026)
ACL