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An intelligent Chatbot using deep learning with Bidirectional RNN and attention
model
#MMPMID32837917
Dhyani M
; Kumar R
Mater Today Proc
2021[]; 34
(?): 817-824
PMID32837917
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This paper shows the modeling and performance in deep learning computation for an
Assistant Conversational Agent (Chatbot). The utilization of Tensorflow software
library, particularly Neural Machine Translation (NMT) model. Acquiring knowledge
for modeling is one of the most important task and quite difficult to preprocess
it. The Bidirectional Recurrent Neural Networks (BRNN) containing attention
layers is used, so that input sentence with large number of tokens (or sentences
with more than 20-40 words) can be replied with more appropriate conversation.
The dataset used in the paper for training of model is used from Reddit. The
model is developed to perform English to English translation. The main purpose of
this work is to increase the perplexity and learning rate of the model and find
Bleu Score for translation in same language. The experiments are conducted using
Tensorflow using python 3.6. The perplexity, leaning rate, Bleu score and Average
time per 1000 steps are 56.10, 0.0001, 30.16 and 4.5 respectively. One epoch is
completed at 23,000 steps. The paper also study MacBook Air as a system for
neural network and deep learning.
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