Bengali Question Answering System Using Seq2Seq Learning Based on General Knowledge Dataset

Abstract

Context-based QA system is a leading research area in NLP. An automatic QA system that can respond to the answer, and which is related to the given context. A deep learning-based model provides a more factual result for today's QA system. Here we introduce a deep learning-based Seq2Seq model for Bengali context-based QA system using general knowledge dataset. Where context and question are part of the encoder and related answer is part of the decoder. All automatic system can discern any language by machine translation. Sequence wise learning is a good solution for those types of automatic system learning. Input tokens are encoded by the encoder and output tokens are decoded by the decoder. Each sequence is stored in LSTM cell that maintains the sequence of input and output. Most of the AI system is developed in different languages. Compared with other languages the Bengali language needs to expand research this field. The major perspective of this research to develop an AI based QA system for the Bengali language. For experiment total, two thousand Bengali general knowledge data is used that also provides a dataset for Bengali QA system. In the dataset, the context contains the main feature for the question. After training our model it gives 99 % accuracy for this dataset and 89% accuracy for validation. The trained model gives a good response in answer prediction.