Deep learning based question answering system in Bengali

Recent advances in the field of natural language processing has improved state-of-the-art performances on many tasks including question answering for languages like English. Bengali language is ranked seventh and is spoken by about 300 million people all over the world. But due to lack of data and active research on QA similar progress has not been achieved for Bengali. Unlike English, there is no benchmark large scale QA dataset collected for Bengali, no pretrained language model that can be modified for Bengali question answering and no human baseline score for QA has been established either. In this work we use state-of-the-art transformer models to train QA system on a synthetic reading comprehension dataset translated from one of the most popular benchmark datasets in English called SQuAD 2.0. We collect a smaller human annotated QA dataset from Bengali Wikipedia with popular topics from Bangladeshi culture for evaluating our models. Finally, we compare our models with human children to set up a benchmark score using survey experiments.