IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages
This addresses the problem of limited QA resources for Indic language speakers, but it is incremental as it builds on existing translation techniques and datasets like MahaSQuAD.
The authors tackled the underrepresentation of Indic languages in question-answering systems by creating IndicSQuAD, a comprehensive multilingual dataset covering nine major Indic languages, derived from SQuAD with high linguistic fidelity and answer-span alignment, and evaluated baseline models showing challenges in low-resource settings.
The rapid progress in question-answering (QA) systems has predominantly benefited high-resource languages, leaving Indic languages largely underrepresented despite their vast native speaker base. In this paper, we present IndicSQuAD, a comprehensive multi-lingual extractive QA dataset covering nine major Indic languages, systematically derived from the SQuAD dataset. Building on previous work with MahaSQuAD for Marathi, our approach adapts and extends translation techniques to maintain high linguistic fidelity and accurate answer-span alignment across diverse languages. IndicSQuAD comprises extensive training, validation, and test sets for each language, providing a robust foundation for model development. We evaluate baseline performances using language-specific monolingual BERT models and the multilingual MuRIL-BERT. The results indicate some challenges inherent in low-resource settings. Moreover, our experiments suggest potential directions for future work, including expanding to additional languages, developing domain-specific datasets, and incorporating multimodal data. The dataset and models are publicly shared at https://github.com/l3cube-pune/indic-nlp