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Neural topic modeling with bidirectional adversarial training
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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.
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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
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) | ||||||||||||
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| 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: |
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| 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: |
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| 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 | ||||||||||||
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