A bio- medical Question Answering system for the Malayalam language using word embedding and Bidirectional Encoder Representation from Transformers.

Conversational search is the dominant intent of Question Answering, which is achieved through different NLP techniques, Deep Learning models. The advent of Transformer models has been a breakthrough in Natural Language Processing applications, which has attained a benchmark on state of NLP tasks such as question answering. Here we propose a semantic Malayalam Question Answering system that automatically answers the queries related to health issues. The Biomedical Question-Answering, especially in the Malayalam language, is a tedious and challenging task. The proposed model uses a neural network-based Bidirectional Encoder Representation from Transformers (BERT), to implement the question answering system. In this study, we investigate how to train and fine-tune a BERT model for Question-Answering. The system has been tested with our own annotated Malayalam SQUAD form health dataset. In comparing the result with our previous works - Word embedding and RNN based model, identified we find that our BERT model is more accurate than the previous models and achieves an F1 score of 86%.