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Building task-oriented dialogue systems for online shopping

  • Zhao Yan*
  • , Nan Duan
  • , Peng Chen
  • , Ming Zhou
  • , Jianshe Zhou
  • , Zhoujun Li
  • *Corresponding author for this work
  • Beihang University
  • Microsoft USA
  • Microsoft Xiaoice Team
  • Capital Normal University

Research output: Contribution to conferencePaperpeer-review

Abstract

We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner. As a pioneering work, we show what & how existing natural language processing techniques, data resources, and crowdsourcing can be leveraged to build such task-oriented dialogue systems for E-commerce usage. To demonstrate its effectiveness, we integrate our system into a mobile online shopping application. To the best of our knowledge, this is the first time that an dialogue system in Chinese is practically used in online shopping scenario with millions of real consumers. Interesting and insightful observations are shown in the experimental part, based on the analysis of human-bot conversation log. Several current challenges are also pointed out as our future directions.

Original languageEnglish
Pages4618-4625
Number of pages8
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco
Period4/02/1710/02/17

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