# Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi

arXiv: https://arxiv.org/abs/2308.09862 · HTML: https://mlsanity.com/paper/2308.09862/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages](https://mlsanity.com/paper/2211.11418/) (2022)
- [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022)
- [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022)
- [MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages](https://mlsanity.com/paper/2204.05814/) (2022)
- [BERT Based Multilingual Machine Comprehension in English and Hindi](https://mlsanity.com/paper/2006.01432/) (2020)

## Built on by (most cited first, 5 of 5)

- [Multilingual State Space Models for Structured Question Answering in Indic Languages](https://mlsanity.com/paper/2502.01673/) (2025)
- [Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5](https://mlsanity.com/paper/2409.05401/) (2024)
- [Tourism Question Answer System in Indian Language using Domain-Adapted Foundation Models](https://mlsanity.com/paper/2511.23235/) (2025)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)
- [LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering](https://mlsanity.com/paper/2601.03025/) (2026)

## Abstract (arXiv)

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.