skip to main content
10.1145/3209978.3210183acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
tutorial

Neural Approaches to Conversational AI

Published:27 June 2018Publication History

ABSTRACT

This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.

References

  1. Hongshen Chen, Xiaorui Liu, Dawei Yin, and Jiliang Tang. 2017. A Survey on Dialogue Systems: Recent Advances and New Frontiers. arXiv preprint arXiv:1711.01731 (2017).Google ScholarGoogle Scholar
  2. Yun-Nung Chen, Asli Celikyilmaz, and Dilek Hakkani-Tür. 2017. Deep Learning for Dialogue Systems. Proceedings of ACL 2017, Tutorial Abstracts (2017), 8--14.Google ScholarGoogle ScholarCross RefCross Ref
  3. Jianfeng Gao. 2017. An Introduction to Deep Learning for Natural Language Processing. In International Summer School on Deep Learning, Bilbao.Google ScholarGoogle Scholar
  4. Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, and Michel Galley. 2018. A Knowledge-Grounded Neural Conversation Model. In AAAI.Google ScholarGoogle Scholar
  5. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In NAACL-HLT.Google ScholarGoogle Scholar
  6. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A Persona-Based Neural Conversation Model. In ACL.Google ScholarGoogle Scholar
  7. Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016. Deep Reinforcement Learning for Dialogue Generation. In EMNLP.Google ScholarGoogle Scholar
  8. Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, and Kam-Fai Wong. 2018. Integrating planning for task-completion dialogue policy learning. arXiv preprint arXiv:1801.06176 (2018).Google ScholarGoogle Scholar
  9. Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, and Joelle Pineau. 2015. A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:1512.05742 (2015).Google ScholarGoogle Scholar
  10. Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016. Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models.. In AAAI. 3776--3784. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Yelong Shen, Po-Sen Huang, Ming-Wei Chang, and Jianfeng Gao. 2017. Traversing Knowledge Graph in Vector Space without Symbolic Space Guidance. arXiv preprint arXiv:1611.04642 (2017).Google ScholarGoogle Scholar
  12. Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. 2015. A neural network approach to context-sensitive generation of conversational responses. In NAACL-HLT.Google ScholarGoogle Scholar
  13. Richard S Sutton. 1990. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the seventh international conference on machine learning. 216--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Richard S. Sutton, Doina Precup, and Satinder P. Singh. 1999. Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artificial Intelligence 112, 1--2 (1999), 181--211. An earlier version appeared as Technical Report 98--74, Department of Computer Science, University of Massachusetts, Amherst, MA 01003. April, 1998.Google ScholarGoogle Scholar
  15. Gokhan Tur and Renato De Mori. 2011. Spoken language understanding: Systems for extracting semantic information from speech. John Wiley & Sons.Google ScholarGoogle Scholar
  16. Oriol Vinyals and Quoc Le. 2015. A Neural Conversational Model. In ICML Deep Learning Workshop.Google ScholarGoogle Scholar
  17. Wen-tau Yih, Xiaodong He, and Jianfeng Gao. 2015. Deep Learning and Continuous Representations for Natural Language Processing. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial.Google ScholarGoogle Scholar
  18. Wen-tau Yih, Xiaodong He, and Jianfeng Gao. 2016. Deep Learning and Continuous Representations for Natural Language Processing. In IJCAI: Tutorial.Google ScholarGoogle Scholar
  19. Steve Young, Milica Gašić, Blaise Thomson, and Jason D Williams. 2013. Pomdpbased statistical spoken dialog systems: A review. Proc. IEEE 101, 5 (2013), 1160-- 1179.Google ScholarGoogle ScholarCross RefCross Ref
  20. Tiancheng Zhao, Allen Lu, Kyusong Lee, and Maxine Eskenazi. 2017. Generative encoder-decoder models for task-oriented spoken dialog systems with chatting capability. arXiv preprint arXiv:1706.08476 (2017).Google ScholarGoogle Scholar
  1. Neural Approaches to Conversational AI

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
      June 2018
      1509 pages
      ISBN:9781450356572
      DOI:10.1145/3209978

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 June 2018

      Check for updates

      Qualifiers

      • tutorial

      Acceptance Rates

      SIGIR '18 Paper Acceptance Rate86of409submissions,21%Overall Acceptance Rate792of3,983submissions,20%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader