Question answering using deep learning in low resource Indian language
  Marathi
Dhiraj Amin Sharvari Govilkar Sagar Kulkarni
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.