# Question answering using deep learning in low resource Indian language Marathi

Source: [Question answering using deep learning in low resource Indian language Marathi](<https://www.sota2.com/research/paper/68fa70af1e209131cea4c86d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:47:58.595Z

Authors: Dhiraj Amin, Sharvari Govilkar, Sagar Kulkarni

Published: 2023-09-27T16:53:11.000Z

## Abstract

Precise answers are extracted from a text for a given input question in a question answering system. Marathi question answering system is created in recent studies by using ontology, rule base and machine learning based approaches. Recently transformer models and transfer learning approaches are used to solve question answering challenges. In this paper we investigate different transformer models for creating a reading comprehension-based Marathi question answering system. We have experimented on different pretrained Marathi language multilingual and monolingual models like Multilingual Representations for Indian Languages (MuRIL), MahaBERT, Indic Bidirectional Encoder Representations from Transformers (IndicBERT) and fine-tuned it on a Marathi reading comprehension-based data set. We got the best accuracy in a MuRIL multilingual model with an EM score of 0.64 and F1 score of 0.74 by fine tuning the model on the Marathi dataset.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2309.15779>)

[PDF](<https://arxiv.org/pdf/2309.15779.pdf>)