End-to-end memory networks: a survey

Raheleh Jafari*, Sina Razvarz, Alexander Gegov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Constructing a dialog system which can speak naturally with a human is considered as a major challenge of artificial intelligence. End-to-end dialog system is taken to be a primary research topic in the area of conversational systems. Since an end-to-end dialog system is structured based on learning a dialog policy from transactional dialogs in a defined extent, therefore, useful datasets are required for evaluating the learning procedures. In this paper, different deep learning techniques are applied to the Dialog bAbI datasets. On this dataset, the performance of the proposed techniques is analyzed. The performance results demonstrate that all the proposed techniques attain decent precisions on the Dialog bAbI datasets. The best performance is obtained utilizing end-to-end memory network with a unified weight tying scheme (UN2N).

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2020 Computing Conference
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
PublisherSpringer
Pages291-300
Number of pages10
ISBN (Print)9783030522452
DOIs
Publication statusPublished - 4 Jul 2020
EventScience and Information Conference - London, United Kingdom
Duration: 16 Jul 202017 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume1229
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceScience and Information Conference
Abbreviated titleSAI 2020
Country/TerritoryUnited Kingdom
CityLondon
Period16/07/2017/07/20

Keywords

  • Deep learning
  • Dialog bAbI dataset
  • Memory networks

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