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Neural topic model with reinforcement learning
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Gui, Lin, Leng, Jia, Pergola, Gabriele, Zhou, Yu, Xu, Ruifeng and He, Yulan (2019) Neural topic model with reinforcement learning. In: 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, 3-7 Nov 2019. Published in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) pp. 3478-3483. doi:10.18653/v1/D19-1350
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Official URL: https://doi.org/10.18653/v1/D19-1350
Abstract
In recent years, advances in neural variational inference have achieved many successes in text processing. Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an objective of minimising the error of reconstructing original documents based on the learned latent topic vectors. However, minimising reconstruction errors does not necessarily lead to high quality topics. In this paper, we borrow the idea of reinforcement learning and incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. Furthermore, our proposed model is able to automatically separating background words dynamically from topic words, thus eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. Experimental results on the 20 Newsgroups and the NIPS datasets show superior performance both on perplexity and topic coherence measure compared to state-of-the-art neural topic models.
Item Type: | Conference Item (Paper) | ||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Text processing (Computer science), Reinforcement learning -- Mathematical models, Neural networks (Computer science) | ||||||||||||
Journal or Publication Title: | Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) | ||||||||||||
Publisher: | Association for Computational Linguistics | ||||||||||||
Official Date: | November 2019 | ||||||||||||
Dates: |
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Page Range: | pp. 3478-3483 | ||||||||||||
DOI: | 10.18653/v1/D19-1350 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Reuse Statement (publisher, data, author rights): | ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 17 September 2019 | ||||||||||||
Date of first compliant Open Access: | 25 February 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||
Title of Event: | 2019 Conference on Empirical Methods in Natural Language Processing | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Hong Kong | ||||||||||||
Date(s) of Event: | 3-7 Nov 2019 | ||||||||||||
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