@inproceedings{munawar-etal-2026-hu,
title = "{HU} at {S}em{E}val-2026 Task 6: A Hybrid Discriminative Modeling of Political Clarity and Evasion",
author = "Munawar, Taha and
Khan, Basil and
Jangda, Arsal and
Baig, Sarfaraz and
Kumar, Sandesh and
Samad, Abdul",
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-1.34/",
pages = "235--241",
ISBN = "979-8-89176-414-9",
abstract = "We describe our submission to SemEval-2026 Task 6: CLARITY, which aims to classify political question{--}answer pairs by response clarity and evasive technique. We investigate several approaches, including long-context transformers, multiple instance learning, hierarchical multi-task models, and a natural language inference (NLI) formulation. On the development set, our best-performing NLI model achieves a macro-F1 of 0.79 for Subtask 1, while our best attention-based MIL model achieves a macro-F1 of 0.43 for Subtask 2. On the hidden evaluation set, our official submission obtains macro-F1 scores of 0.81 for Subtask 1 and 0.45 for Subtask 2. Our findings demonstrate the benefits of entailment-based modeling for clarity prediction and localized reasoning for evasion detection under limited computational resources."
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<abstract>We describe our submission to SemEval-2026 Task 6: CLARITY, which aims to classify political question–answer pairs by response clarity and evasive technique. We investigate several approaches, including long-context transformers, multiple instance learning, hierarchical multi-task models, and a natural language inference (NLI) formulation. On the development set, our best-performing NLI model achieves a macro-F1 of 0.79 for Subtask 1, while our best attention-based MIL model achieves a macro-F1 of 0.43 for Subtask 2. On the hidden evaluation set, our official submission obtains macro-F1 scores of 0.81 for Subtask 1 and 0.45 for Subtask 2. Our findings demonstrate the benefits of entailment-based modeling for clarity prediction and localized reasoning for evasion detection under limited computational resources.</abstract>
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%0 Conference Proceedings
%T HU at SemEval-2026 Task 6: A Hybrid Discriminative Modeling of Political Clarity and Evasion
%A Munawar, Taha
%A Khan, Basil
%A Jangda, Arsal
%A Baig, Sarfaraz
%A Kumar, Sandesh
%A Samad, Abdul
%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 munawar-etal-2026-hu
%X We describe our submission to SemEval-2026 Task 6: CLARITY, which aims to classify political question–answer pairs by response clarity and evasive technique. We investigate several approaches, including long-context transformers, multiple instance learning, hierarchical multi-task models, and a natural language inference (NLI) formulation. On the development set, our best-performing NLI model achieves a macro-F1 of 0.79 for Subtask 1, while our best attention-based MIL model achieves a macro-F1 of 0.43 for Subtask 2. On the hidden evaluation set, our official submission obtains macro-F1 scores of 0.81 for Subtask 1 and 0.45 for Subtask 2. Our findings demonstrate the benefits of entailment-based modeling for clarity prediction and localized reasoning for evasion detection under limited computational resources.
%U https://aclanthology.org/2026.semeval-1.34/
%P 235-241
Markdown (Informal)
[HU at SemEval-2026 Task 6: A Hybrid Discriminative Modeling of Political Clarity and Evasion](https://aclanthology.org/2026.semeval-1.34/) (Munawar et al., SemEval 2026)
ACL