Deep Learning for Dialogue Systems

Yun Nung Chen, Asli Celikyilmaz, Dilek Hakkani-Tür

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

Abstract

Goal-oriented spoken dialogue systems have been the most prominent component in todays virtual personal assistants, which allow users to speak naturally in order to finish tasks more efficiently. The advancement of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge of the prior work and the recent state-of-the-art work. Therefore, this tutorial is designed to focus on an overview of dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab.tw.

Original languageEnglish (US)
Title of host publicationCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of Tutorial Abstracts
PublisherAssociation for Computational Linguistics (ACL)
Pages25-31
Number of pages7
ISBN (Electronic)9781948087520
StatePublished - 2018
Externally publishedYes
Event27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States
Duration: Aug 20 2018Aug 26 2018

Publication series

NameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of Tutorial Abstracts

Conference

Conference27th International Conference on Computational Linguistics, COLING 2018
Country/TerritoryUnited States
CitySanta Fe
Period8/20/188/26/18

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'Deep Learning for Dialogue Systems'. Together they form a unique fingerprint.

Cite this