@inproceedings{calvo-bartolome-etal-2025-discrepancy,
    title = "Discrepancy Detection at the Data Level: Toward Consistent Multilingual Question Answering",
    author = "Calvo-Bartolom{\'e}, Lorena  and
      Aldana, Val{\'e}rie  and
      Cantarero, Karla  and
      de Mesa, Alonso Madro{\~n}al  and
      Arenas-Garc{\'i}a, Jer{\'o}nimo  and
      Boyd-Graber, Jordan Lee",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1120/",
    pages = "22024--22065",
    ISBN = "979-8-89176-332-6",
    abstract = "Multilingual question answering (QA) systems must ensure factual consistency across languages, especially for objective queries such as What is jaundice?, while also accounting for cultural variation in subjective responses. We propose MIND, a user-in-the-loop fact-checking pipeline to detect factual and cultural discrepancies in multilingual QA knowledge bases. MIND highlights divergent answers to culturally sensitive questions (e.g., Who assists in childbirth?) that vary by region and context. We evaluate MIND on a bilingual QA system in the maternal and infant health domain and release a dataset of bilingual questions annotated for factual and cultural inconsistencies. We further test MIND on datasets from other domains to assess generalization. In all cases, MIND reliably identifies inconsistencies, supporting the development of more culturally aware and factually consistent QA systems."
}