TY - JFULL AU - Qitao Xie and Qingquan Zhang and Xiaofei Zhang and Di Tian and Ruixuan Wen and Ting Zhu and Ping Yi and Xin Li PY - 2021/3/ TI - A Context-Centric Chatbot for Cryptocurrency Using the Bidirectional Encoder Representations from Transformers Neural Networks T2 - International Journal of Economics and Management Engineering SP - 149 EP - 156 VL - 15 SN - 1307-6892 UR - https://publications.waset.org/pdf/10011865 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 170, 2021 N2 - Inspired by the recent movement of digital currency, we are building a question answering system concerning the subject of cryptocurrency using Bidirectional Encoder Representations from Transformers (BERT). The motivation behind this work is to properly assist digital currency investors by directing them to the corresponding knowledge bases that can offer them help and increase the querying speed. BERT, one of newest language models in natural language processing, was investigated to improve the quality of generated responses. We studied different combinations of hyperparameters of the BERT model to obtain the best fit responses. Further, we created an intelligent chatbot for cryptocurrency using BERT. A chatbot using BERT shows great potential for the further advancement of a cryptocurrency market tool. We show that the BERT neural networks generalize well to other tasks by applying it successfully to cryptocurrency. ER -