Investigating Deep Learning for Predicting Multi-linguistic Interactions with a Chatterbot

R. Kulesza, Y. Kumar, R. Ruiz, A. Torres, E. Weinman, J. J. Li, P. Morreale

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Deep Learning (DL) becomes a mainstream technique for Artificial Intelligence (AI) machine learning because of its success in performing many tasks, such as image recognition, speech interpretation, language prediction and translation. We are investigating the underlying principles of DL Neural Networks (NN) to design optimal DL NN for predicting human multi-linguistic conversations with a chatterbot. This research attempts to tackle the well-known open problem of finding optimal NN designs for data of various characteristics. We are in particular focusing on Recurrent Neural Networks (RNN) models with time progression, which takes into consideration the results from the previous steps plus the current input to predict the next step, i.e. it 'remembers' what it has previously learnt. Through the experiments of tuning an RNN to achieve an optimal performance in terms of accuracy and training time, we found that characteristics such as word counts and layers of neurons could affect the training performance. We applied the tuned optimal model to a game implementation, inspired by IBM Watson, where users can guess the words to be generated by a computer, called 'Beat AI' to have human predict the machine prediction.

Original languageEnglish
Title of host publication2020 IEEE Conference on Big Data and Analytics, ICBDA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages20-25
Number of pages6
ISBN (Electronic)9781728192468
DOIs
StatePublished - 17 Nov 2020
Event2020 IEEE Conference on Big Data and Analytics, ICBDA 2020 - Kota Kinabalu, Malaysia
Duration: 17 Nov 202019 Nov 2020

Publication series

Name2020 IEEE Conference on Big Data and Analytics, ICBDA 2020

Conference

Conference2020 IEEE Conference on Big Data and Analytics, ICBDA 2020
Country/TerritoryMalaysia
CityKota Kinabalu
Period17/11/2019/11/20

Keywords

  • Deep Learning
  • Keras
  • Machine Learning
  • Neural Networks
  • RNN
  • TensorFlow
  • Yandex Predictor

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