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An AI chatbot provides an impressive response after learning from the traineddataset. In this decade, most of the research work demonstrates that deepneural models superior to any other model. RNN model regularly used fordetermining the sequence-related problem like a question and it answers. Thisapproach acquainted with everyone as seq2seq learning. In a seq2seq modelmechanism, it has encoder and decoder. The encoder embedded any input sequence,and the decoder embedded output sequence. For reinforcing the seq2seq modelperformance, attention mechanism added into the encoder and decoder. Afterthat, the transformer model has introduced itself as a high-performance modelwith multiple attention mechanism for solving the sequence-related dilemma.This model reduces training time compared with RNN based model and alsoachieved state-of-the-art performance for sequence transduction. In thisresearch, we applied the transformer model for Bengali general knowledgechatbot based on the Bengali general knowledge Question Answer (QA) dataset. Itscores 85.0 BLEU on the applied QA data. To check the comparison of thetransformer model performance, we trained the seq2seq model with attention onour dataset that scores 23.5 BLEU.

Transformer Based Bengali Chatbot Using General Knowledge Dataset