@inproceedings{jimenez-alvear-etal-2026-verbanexai,
title = "{V}erba{N}ex{AI} at {S}em{E}val-2026 Task 6: Automatic Detection of Political Evasion through Hierarchical Classification with {R}o{BERT}a Large",
author = "Jimenez Alvear, Jeison and
G{\'o}mez S{\'a}nchez, Deyson and
Martinez Santos, Juan Carlos and
Puertas, Edwin and
Serrano, Jairo",
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-1.185/",
pages = "1429--1435",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes VerbaNex AI{'}s participation in SemEval-2026 Task 6: CLARITY, a shared task on automatic detection of question evasion in political interview transcripts. The task requires classifying question-answer pairs into three clarity levels (Task 1) and identifying nine evasion techniques (Task 2). We propose and evaluate two independent systems based on RoBERTa-Large. The first is a standard sequence classifier that treats each question-answer pair as a single input sequence, leveraging RoBERTa{'}s native two-segment encoding to model the relationship between the two texts jointly. The second is a dual-encoder architecture that processes the question and answer independently and computes geometric interaction features to model the semantic misalignment between them explicitly. Both systems are trained on Task 2 labels and derive Task 1 predictions via the hierarchical mapping proposed by the task organizers. Our best result was achieved by the standard sequence classifier, reaching Rank 10 on Task 2 and Rank 25 on Task 1."
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<abstract>This paper describes VerbaNex AI’s participation in SemEval-2026 Task 6: CLARITY, a shared task on automatic detection of question evasion in political interview transcripts. The task requires classifying question-answer pairs into three clarity levels (Task 1) and identifying nine evasion techniques (Task 2). We propose and evaluate two independent systems based on RoBERTa-Large. The first is a standard sequence classifier that treats each question-answer pair as a single input sequence, leveraging RoBERTa’s native two-segment encoding to model the relationship between the two texts jointly. The second is a dual-encoder architecture that processes the question and answer independently and computes geometric interaction features to model the semantic misalignment between them explicitly. Both systems are trained on Task 2 labels and derive Task 1 predictions via the hierarchical mapping proposed by the task organizers. Our best result was achieved by the standard sequence classifier, reaching Rank 10 on Task 2 and Rank 25 on Task 1.</abstract>
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%0 Conference Proceedings
%T VerbaNexAI at SemEval-2026 Task 6: Automatic Detection of Political Evasion through Hierarchical Classification with RoBERTa Large
%A Jimenez Alvear, Jeison
%A Gómez Sánchez, Deyson
%A Martinez Santos, Juan Carlos
%A Puertas, Edwin
%A Serrano, Jairo
%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 jimenez-alvear-etal-2026-verbanexai
%X This paper describes VerbaNex AI’s participation in SemEval-2026 Task 6: CLARITY, a shared task on automatic detection of question evasion in political interview transcripts. The task requires classifying question-answer pairs into three clarity levels (Task 1) and identifying nine evasion techniques (Task 2). We propose and evaluate two independent systems based on RoBERTa-Large. The first is a standard sequence classifier that treats each question-answer pair as a single input sequence, leveraging RoBERTa’s native two-segment encoding to model the relationship between the two texts jointly. The second is a dual-encoder architecture that processes the question and answer independently and computes geometric interaction features to model the semantic misalignment between them explicitly. Both systems are trained on Task 2 labels and derive Task 1 predictions via the hierarchical mapping proposed by the task organizers. Our best result was achieved by the standard sequence classifier, reaching Rank 10 on Task 2 and Rank 25 on Task 1.
%U https://aclanthology.org/2026.semeval-1.185/
%P 1429-1435
Markdown (Informal)
[VerbaNexAI at SemEval-2026 Task 6: Automatic Detection of Political Evasion through Hierarchical Classification with RoBERTa Large](https://aclanthology.org/2026.semeval-1.185/) (Jimenez Alvear et al., SemEval 2026)
ACL