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tutorial

Deep Chit-Chat: Deep Learning for Chatbots

Published:18 July 2019Publication History

ABSTRACT

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.

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  1. Deep Chit-Chat: Deep Learning for Chatbots

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    • Published in

      cover image ACM Conferences
      SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
      July 2019
      1512 pages
      ISBN:9781450361729
      DOI:10.1145/3331184

      Copyright © 2019 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: 18 July 2019

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      Qualifiers

      • tutorial

      Acceptance Rates

      SIGIR'19 Paper Acceptance Rate84of426submissions,20%Overall Acceptance Rate792of3,983submissions,20%

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