@inproceedings{riad-2026-syntaxmind,
title = "{S}yntax{M}ind at {S}em{E}val-2026 Task 6: Exploring Transformers and {LLM}s for Unmasking Political Question Evasions",
author = "Riad, Md. Shihab Uddin",
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-1.419/",
pages = "3377--3381",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes our approach to Subtask 1: Clarity-level Classification in SemEval-2026 Task 6. The task focuses on determining the clarity of political responses with respect to their corresponding questions. To enhance model performance, we introduced a direct answer generation strategy as an additional input feature and applied Task-Adaptive Pre-Training (TAPT) to enhance encoder-only Transformer models with the task domain. We further explored both cross-entropy and focal loss to address potential class imbalance. Experimental results show that TAPT enhanced encoder models, particularly DeBERTa-V3-base, achieved the strongest performance, while generative small language models fine-tuned via parameter-efficient methods exhibited comparatively lower results. Our system obtained a macro-F1 score of 0.72 on the official evaluation set, ranking 24th out of 40 teams."
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<abstract>This paper describes our approach to Subtask 1: Clarity-level Classification in SemEval-2026 Task 6. The task focuses on determining the clarity of political responses with respect to their corresponding questions. To enhance model performance, we introduced a direct answer generation strategy as an additional input feature and applied Task-Adaptive Pre-Training (TAPT) to enhance encoder-only Transformer models with the task domain. We further explored both cross-entropy and focal loss to address potential class imbalance. Experimental results show that TAPT enhanced encoder models, particularly DeBERTa-V3-base, achieved the strongest performance, while generative small language models fine-tuned via parameter-efficient methods exhibited comparatively lower results. Our system obtained a macro-F1 score of 0.72 on the official evaluation set, ranking 24th out of 40 teams.</abstract>
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%0 Conference Proceedings
%T SyntaxMind at SemEval-2026 Task 6: Exploring Transformers and LLMs for Unmasking Political Question Evasions
%A Riad, Md. Shihab Uddin
%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 riad-2026-syntaxmind
%X This paper describes our approach to Subtask 1: Clarity-level Classification in SemEval-2026 Task 6. The task focuses on determining the clarity of political responses with respect to their corresponding questions. To enhance model performance, we introduced a direct answer generation strategy as an additional input feature and applied Task-Adaptive Pre-Training (TAPT) to enhance encoder-only Transformer models with the task domain. We further explored both cross-entropy and focal loss to address potential class imbalance. Experimental results show that TAPT enhanced encoder models, particularly DeBERTa-V3-base, achieved the strongest performance, while generative small language models fine-tuned via parameter-efficient methods exhibited comparatively lower results. Our system obtained a macro-F1 score of 0.72 on the official evaluation set, ranking 24th out of 40 teams.
%U https://aclanthology.org/2026.semeval-1.419/
%P 3377-3381
Markdown (Informal)
[SyntaxMind at SemEval-2026 Task 6: Exploring Transformers and LLMs for Unmasking Political Question Evasions](https://aclanthology.org/2026.semeval-1.419/) (Riad, SemEval 2026)
ACL