@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://preview.aclanthology.org/ingest-acl-workshops/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."
}