Building Task-Oriented Dialogue Systems for Online Shopping

Authors

  • Zhao Yan Beihang University
  • Nan Duan Microsoft Research
  • Peng Chen Microsoft
  • Ming Zhou Microsoft Research
  • Jianshe Zhou Capital Normal University
  • Zhoujun Li Beihang University

DOI:

https://doi.org/10.1609/aaai.v31i1.11182

Keywords:

Dialogue System, Natural Language Processing, Knowledge Acquisition

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 NLP 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 app. To the best of our knowledge, this is the first time that an AI bot 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.

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Published

2017-02-12

How to Cite

Yan, Z., Duan, N., Chen, P., Zhou, M., Zhou, J., & Li, Z. (2017). Building Task-Oriented Dialogue Systems for Online Shopping. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11182