  The recent advances in deep-learning have led to the development of highly
sophisticated systems with an unquenchable appetite for data. On the other
hand, building good deep-learning models for low-resource languages remains a
challenging task. This paper focuses on developing a Question Answering dataset
for two such languages- Hindi and Marathi. Despite Hindi being the 3rd most
spoken language worldwide, with 345 million speakers, and Marathi being the
11th most spoken language globally, with 83.2 million speakers, both languages
face limited resources for building efficient Question Answering systems. To
tackle the challenge of data scarcity, we have developed a novel approach for
translating the SQuAD 2.0 dataset into Hindi and Marathi. We release the
largest Question-Answering dataset available for these languages, with each
dataset containing 28,000 samples. We evaluate the dataset on various
architectures and release the best-performing models for both Hindi and
Marathi, which will facilitate further research in these languages. Leveraging
similarity tools, our method holds the potential to create datasets in diverse
languages, thereby enhancing the understanding of natural language across
varied linguistic contexts. Our fine-tuned models, code, and dataset will be
made publicly available.
