April 23, 2024, 4:42 a.m. | Ruturaj Ghatage, Aditya Kulkarni, Rajlaxmi Patil, Sharvi Endait, Raviraj Joshi

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.13364v1 Announce Type: cross
Abstract: Question-answering systems have revolutionized information retrieval, but linguistic and cultural boundaries limit their widespread accessibility. This research endeavors to bridge the gap of the absence of efficient QnA datasets in low-resource languages by translating the English Question Answering Dataset (SQuAD) using a robust data curation approach. We introduce MahaSQuAD, the first-ever full SQuAD dataset for the Indic language Marathi, consisting of 118,516 training, 11,873 validation, and 11,803 test samples. We also present a gold test …

arxiv cs.cl cs.lg question type

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