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Supervised Term Weight Training for Improving Question-Knowledge Matching in Chatbots

Authors:

Shuangyong Song,

Chao Wang,

Haiqing Chen,

Huan ChenAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 13 - 14

https://doi.org/10.1145/3366424.3382674

Published: 20 April 2020 Publication History

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Abstract

We design several supervised methods of training term weights for improving results of question-knowledge matching task on a chatbot system-AliMe. Considering demand of high QPS (Query Per Second), we take a traditional regression model as the matching model rather than deep models.

References

[1]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.

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Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. 2015. From word embeddings to document distances. In International conference on machine learning. 957–966.

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Mohammad Masudur Rahman and Chanchal K Roy. 2017. Improved query reformulation for concept location using coderank and document structures. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 428–439.

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[4]

Shuangyong Song, Haiqing Chen, and Zhiwei Shi. 2017. Intension classification of user queries in intelligent customer service system. In 2017 International Conference on Asian Language Processing (IALP). IEEE, 83–86.

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Index Terms

Supervised Term Weight Training for Improving Question-Knowledge Matching in Chatbots
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Abstract

References

Index Terms

Recommendations

Evaluating and Informing the Design of Chatbots

Weakly Supervised Co-Training of Query Rewriting andSemantic Matching for e-Commerce

Semantic Question Matching in Data Constrained Environment

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