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Implementation of Artificial Intelligence Based Chatbot System with Long Term Memory

EasyChair Preprint no. 3202

5 pagesDate: April 20, 2020


This paper mainly explores a specific deep learning method to build a conversational agent. Nowadays the popularity of chatbot systems is on rise as they attempt to get into daily life and achieve some commercial success. Previous approaches used simple keywords & pattern matching methodologies, answering in a static manner irrespective of previous conversions. As an improvement to this technology would be a system that will work with sequence to sequence framework. Our proposed model makes use of this framework. Given the previous sentence or sentences and the next sentence in a conversation, the model converses by predicting the next sentence. The distinctive feature of our model is that it can be trained end-to-end hence requires much fewer hand-crafted rules. This straightforward model can generate simple conversations given a large conversational training dataset.

Keyphrases: deep learning, end to end memory, LSTM model, Recurrent Neural Network, seq-to-seq model

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shruti Katkade and Urjita Kerkar and Pravin Bhilare and Manish Gupta and Payel Thakur},
  title = {Implementation of Artificial Intelligence Based Chatbot System with Long Term Memory},
  howpublished = {EasyChair Preprint no. 3202},

  year = {EasyChair, 2020}}
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