MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering

TL;DR

This paper introduces MahaSQuAD, a comprehensive Marathi question-answering dataset derived from translating English SQuAD, along with a robust translation approach to support low-resource language QA systems.

Contribution

It presents the first Marathi SQuAD dataset and a scalable translation method for low-resource languages, addressing linguistic nuances and context preservation.

Findings

MahaSQuAD contains over 118,000 training samples.

A novel translation and span-mapping approach was developed.

Datasets and models are publicly available.

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 set of manually verified 500 examples. Challenges in maintaining context and handling linguistic nuances are addressed, ensuring accurate translations. Moreover, as a QnA dataset cannot be simply converted into any low-resource language using translation, we need a robust method to map the answer translation to its span in the translated passage. Hence, to address this challenge, we also present a generic approach for translating SQuAD into any low-resource language. Thus, we offer a scalable approach to bridge linguistic and cultural gaps present in low-resource languages, in the realm of question-answering systems. The datasets and models are shared publicly at https://github.com/l3cube-pune/MarathiNLP .