Chatbot Analytics Based on Question Answering System and Deep Learning: Case Study for Movie Smart Automatic Answering
AUTHORS
Jugal Shah,Computer Science Department, Lakehead University, Ontario, Canada
Sabah Mohammed*,Computer Science Department, Lakehead University, Ontario, Canada
ABSTRACT
Question Answer (QA) systems are established to retrieves accurate and concise answers to human queries posted in natural language. The primary focus of the QA system is to achieve efficient and natural interaction between machines and humans. To achieve the above several researchers are directed towards Natural Language Processing (NLP) based deep learning. With the rise of a variety of deep NLP models, it is now possible to obtain a vector form of words and sentences that stores the meaning of the context. NLP considerably aids deep learning-based mathematical models in understanding the semantic and syntax of natural human language. The Cornell Movie-Dialogs Corpus created at Cornell University, and Movie Dialog Dataset created at Facebook are preprocessed and used to train the chatbot. Deep learning model has been built to answer questions about movies from Moview reviews. The encoder and decoder of the Seq2Seq model comprise of LSTM cells and are defined using Bidirectional Dynamic RNN and Dynamic Decoder RNN package of the tensor flow library. Additionally, to ensure the chatbot performs well on long sentences attention mechanism from the tensor flow library is applied to the decoder. In this paper, research is conducted on build a smart chatbots based QA system that employs a deep learning model. The deep learning model employs a sequence-to-sequence (Seq2Seq) word embedding that was proposed by Ilya Sutskever in 2014, which had laid the foundation for building chatbot model build in this paper.
KEYWORDS
QA, LSTM, RNN, Seq2Seq, Deep learning, Chatboats
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