@inproceedings{pascu-etal-2026-asetclarity,
title = "asetclarity at {S}em{E}val-2026 Task 6: An Imbalance-Aware {R}o{BERT}a Cross-Encoder for Political Response Clarity Classification",
author = "Pascu, Maria-Antonia-Emanuela and
Dodun-des-Perrieres, Dan and
Gifu, Daniela",
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-1.146/",
pages = "1068--1074",
ISBN = "979-8-89176-414-9",
abstract = "We address response-clarity classification in political interviews as defined in SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions, Task 1, where systems must label each question{--}answer pair as Clear Reply, Ambivalent, or Clear Non-Reply. We present a reproducible end-to-end pipeline built around a single-stream RoBERTa-large cross-encoder fine-tuned for three-way classification using deterministic text normalization, concatenated QA inputs, and imbalance-aware training (minority oversampling and class-weighted loss). To improve robustness, we train a 5-fold stratified ensemble and combine models via soft-voting. Our official shared-task submission obtains 0.76 macro-F1 on the official leaderboard, ranking 16 out of 41 participating systems. Finally, we deploy the classifier in a lightweight web application supporting both direct text input and audio-based analysis through automatic transcription, enabling interactive inspection of predicted clarity categories."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pascu-etal-2026-asetclarity">
<titleInfo>
<title>asetclarity at SemEval-2026 Task 6: An Imbalance-Aware RoBERTa Cross-Encoder for Political Response Clarity Classification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria-Antonia-Emanuela</namePart>
<namePart type="family">Pascu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dan</namePart>
<namePart type="family">Dodun-des-Perrieres</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniela</namePart>
<namePart type="family">Gifu</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 address response-clarity classification in political interviews as defined in SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions, Task 1, where systems must label each question–answer pair as Clear Reply, Ambivalent, or Clear Non-Reply. We present a reproducible end-to-end pipeline built around a single-stream RoBERTa-large cross-encoder fine-tuned for three-way classification using deterministic text normalization, concatenated QA inputs, and imbalance-aware training (minority oversampling and class-weighted loss). To improve robustness, we train a 5-fold stratified ensemble and combine models via soft-voting. Our official shared-task submission obtains 0.76 macro-F1 on the official leaderboard, ranking 16 out of 41 participating systems. Finally, we deploy the classifier in a lightweight web application supporting both direct text input and audio-based analysis through automatic transcription, enabling interactive inspection of predicted clarity categories.</abstract>
<identifier type="citekey">pascu-etal-2026-asetclarity</identifier>
<location>
<url>https://aclanthology.org/2026.semeval-1.146/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>1068</start>
<end>1074</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T asetclarity at SemEval-2026 Task 6: An Imbalance-Aware RoBERTa Cross-Encoder for Political Response Clarity Classification
%A Pascu, Maria-Antonia-Emanuela
%A Dodun-des-Perrieres, Dan
%A Gifu, Daniela
%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 pascu-etal-2026-asetclarity
%X We address response-clarity classification in political interviews as defined in SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions, Task 1, where systems must label each question–answer pair as Clear Reply, Ambivalent, or Clear Non-Reply. We present a reproducible end-to-end pipeline built around a single-stream RoBERTa-large cross-encoder fine-tuned for three-way classification using deterministic text normalization, concatenated QA inputs, and imbalance-aware training (minority oversampling and class-weighted loss). To improve robustness, we train a 5-fold stratified ensemble and combine models via soft-voting. Our official shared-task submission obtains 0.76 macro-F1 on the official leaderboard, ranking 16 out of 41 participating systems. Finally, we deploy the classifier in a lightweight web application supporting both direct text input and audio-based analysis through automatic transcription, enabling interactive inspection of predicted clarity categories.
%U https://aclanthology.org/2026.semeval-1.146/
%P 1068-1074
Markdown (Informal)
[asetclarity at SemEval-2026 Task 6: An Imbalance-Aware RoBERTa Cross-Encoder for Political Response Clarity Classification](https://aclanthology.org/2026.semeval-1.146/) (Pascu et al., SemEval 2026)
ACL