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