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.

翻译：在问答系统中，针对给定的输入问题从文本中提取精确答案。近年来的研究通过使用本体、规则库和基于机器学习的方法构建了马拉地语问答系统。近年来，Transformer模型和迁移学习方法被用于解决问答挑战。本文研究了不同Transformer模型以构建基于阅读理解机制的马拉地语问答系统。我们对多种预训练的马拉地语多语言和单语言模型（如印度语言多语言表示模型MuRIL、MahaBERT、Indic双向编码器表示从变换器IndicBERT）进行了实验，并在基于阅读理解的马拉地语数据集上进行了微调。通过在马拉地语数据集上微调MuRIL多语言模型，我们获得了最佳准确率，其EM得分为0.64，F1得分为0.74。