@inproceedings{hamano-etal-2026-ufg,
title = "{UFG}-Semantic at {S}em{E}val-2026 Task 6: {CLARITY} - Unmasking Political Question Evasions",
author = "Hamano, Aline and
Felicio, Beatriz and
Galv{\~a}o, Henrique and
Da Silva, N{\'a}dia",
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-1.300/",
pages = "2384--2393",
ISBN = "979-8-89176-414-9",
abstract = "We propose an approach for Task 6: CLARITY - Unmasking Political Question Evasions. We make use of data augmentation, supervised fine-tuning, and model benchmarking to detect and classify response ambiguity in political discourse. Building on well-founded theory on equivocation and leveraging recent advancements in language modeling, our system was structured based on question/answer (QA) pairs extracted from presidential interviews, and it was evaluated in Clarity-level Classification and Evasion-level Classification."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hamano-etal-2026-ufg">
<titleInfo>
<title>UFG-Semantic at SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aline</namePart>
<namePart type="family">Hamano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Beatriz</namePart>
<namePart type="family">Felicio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henrique</namePart>
<namePart type="family">Galvão</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nádia</namePart>
<namePart type="family">Da Silva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Kochmar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">North</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-414-9</identifier>
</relatedItem>
<abstract>We propose an approach for Task 6: CLARITY - Unmasking Political Question Evasions. We make use of data augmentation, supervised fine-tuning, and model benchmarking to detect and classify response ambiguity in political discourse. Building on well-founded theory on equivocation and leveraging recent advancements in language modeling, our system was structured based on question/answer (QA) pairs extracted from presidential interviews, and it was evaluated in Clarity-level Classification and Evasion-level Classification.</abstract>
<identifier type="citekey">hamano-etal-2026-ufg</identifier>
<location>
<url>https://aclanthology.org/2026.semeval-1.300/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>2384</start>
<end>2393</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UFG-Semantic at SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions
%A Hamano, Aline
%A Felicio, Beatriz
%A Galvão, Henrique
%A Da Silva, Nádia
%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 hamano-etal-2026-ufg
%X We propose an approach for Task 6: CLARITY - Unmasking Political Question Evasions. We make use of data augmentation, supervised fine-tuning, and model benchmarking to detect and classify response ambiguity in political discourse. Building on well-founded theory on equivocation and leveraging recent advancements in language modeling, our system was structured based on question/answer (QA) pairs extracted from presidential interviews, and it was evaluated in Clarity-level Classification and Evasion-level Classification.
%U https://aclanthology.org/2026.semeval-1.300/
%P 2384-2393
Markdown (Informal)
[UFG-Semantic at SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions](https://aclanthology.org/2026.semeval-1.300/) (Hamano et al., SemEval 2026)
ACL