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An Integrated Deep Generative Model for Text Classification and Generation
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Text classification and generation are two important tasks in the field of natural language processing.
In this paper, we deal with both tasks via Variational Autoencoder, which is a powerful deep generative model.
The self-attention mechanism is introduced to the encoder.
The modified encoder extracts the global feature of the input text to produce the hidden code, and we train a neural network classifier based on the hidden code to perform the classification.
On the other hand, the label of the text is fed into the decoder explicitly to enhance the categorization information, which could help with text generation.
The experiments have shown that our model could achieve competitive classification results and the generated text is realistic.
Thus the proposed integrated deep generative model could be an alternative for both tasks.
American Psychological Association (APA)
Wang, Zheng& Wu, Qingbiao. 2018. An Integrated Deep Generative Model for Text Classification and Generation. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1208926
Modern Language Association (MLA)
Wang, Zheng& Wu, Qingbiao. An Integrated Deep Generative Model for Text Classification and Generation. Mathematical Problems in Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1208926
American Medical Association (AMA)
Wang, Zheng& Wu, Qingbiao. An Integrated Deep Generative Model for Text Classification and Generation. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1208926
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208926