Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Neural topic modeling with bidirectional adversarial training

Tools
- Tools
+ Tools

Wang, Rui, Hu, Xuemeng, Zhou, Deyu, He, Yulan, Xiong, Yuxuan, Ye, Chenchen and Xu, Haiyang (2020) Neural topic modeling with bidirectional adversarial training. In: The 58th annual meeting of the Association for Computational Linguistics (ACL), Virtual conference, 5-10 Jul 2020. Published in: Proceedings of The 58th annual meeting of the Association for Computational Linguistics (ACL) pp. 340-350. ISBN 9781952148255.

[img]
Preview
PDF
WRAP-Neural-topic-modeling-bidirectional-adversarial-training-He-2020.pdf - Accepted Version - Requires a PDF viewer.

Download (1227Kb) | Preview
Official URL: https://www.aclweb.org/anthology/2020.acl-main.pdf

Request Changes to record.

Abstract

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Natural language processing (Computer science), Gaussian distribution, Neural computers
Journal or Publication Title: Proceedings of The 58th annual meeting of the Association for Computational Linguistics (ACL)
Publisher: Association for Computational Linguistics (ACL)
ISBN: 9781952148255
Official Date: 2020
Dates:
DateEvent
2020Published
3 April 2020Accepted
Page Range: pp. 340-350
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: 2020 The Association for Computational Linguistics
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/T017112/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
2017YFB1002801National Key Research and Development Program of ChinaUNSPECIFIED
61772132[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
Conference Paper Type: Paper
Title of Event: The 58th annual meeting of the Association for Computational Linguistics (ACL)
Type of Event: Conference
Location of Event: Virtual conference
Date(s) of Event: 5-10 Jul 2020
Related URLs:
  • Organisation

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us