Discrepancy Detection at the Data Level: Toward Consistent Multilingual Question Answering
This addresses the need for more culturally aware and factually consistent QA systems, particularly for objective queries and culturally sensitive topics, though it appears incremental as it builds on existing fact-checking and discrepancy detection methods.
The paper tackles the problem of ensuring factual consistency and accounting for cultural variation in multilingual question answering systems, proposing MIND, a user-in-the-loop fact-checking pipeline that reliably identifies inconsistencies in datasets, including in maternal and infant health and other domains.
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.