@inproceedings{poswiata-etal-2025-unveiling,
    title = "Unveiling Dual Quality in Product Reviews: An {NLP}-Based Approach",
    author = "Po{\'s}wiata, Rafa{\l}  and
      Miro{\'n}czuk, Marcin Micha{\l}  and
      Dadas, S{\l}awomir  and
      Gr{\k{e}}bowiec, Ma{\l}gorzata  and
      Pere{\l}kiewicz, Micha{\l}",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.acl-industry.105/",
    pages = "1480--1497",
    ISBN = "979-8-89176-288-6",
    abstract = "Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address this issue, automated techniques are needed. This paper explores how natural language processing (NLP) can aid in detecting such discrepancies and presents the full process of developing a solution. First, we describe in detail the creation of a new Polish-language dataset with 1,957 reviews, 540 highlighting dual quality issues. We then discuss experiments with various approaches like SetFit with sentence-transformers, transformer-based encoders, and LLMs, including error analysis and robustness verification. Additionally, we evaluate multilingual transfer using a subset of opinions in English, French, and German. The paper concludes with insights on deployment and practical applications."
}