Chatbox for Cryptocurrency
Led by Ting Zhu, Ph. D.
Ting Zhu is an associate professor in the Department of Computer Science and Electrical Engineering (CSEE). He has received NSF CAREER award in 2017. He has dual appointments and serves as an adviser to students in both the Electrical and Computer Engineering (ECE) program and Computer Science (CS) program. His research areas include Big Data, Embedded Systems, Cyber-Physical Systems, Mobile Systems, Distributed Systems, Operating Systems, Renewable and Sustainable Energies, Internet of Things, Wireless and Sensor Networks, Network Protocols, Social Networks, and Security.
The project is concerned with building an intelligent chatbot on the field of cryptocurrency or other hot fields via deep learning. BERT is one such pre-trained model developed by Google which can be fine-tuned to new data and used to create NLP systems like question answering, text generation, text classification, text summarization, and sentiment analysis. As BERT is pretrained on huge amounts of data, the process of language modeling is easier. The main benefit for using a pre-trained model of BERT is substantial accuracy improvement compared to training on these datasets from scratch. The central question that this project asks, then, is: how to improve the results of chatbot? A few steps we have been doing: collected data via scraping quora and related websites and pre-processed the data, then used BERT to fine-tune data. We also implemented chatbot based on BERT. In addition, we tuned up different hyperparameters to obtain the optimal results. Next, we explore whether a new hybrid deep network model can predict the next statement and how it will perform. We explore new algorithms or modify the existing algorithms to improve the chatbot performance. Future work will explore one-shot learning algorithm to perform sentiment analysis on our chatbot.