Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation

Md. Akmal Haidar, Mehdi Rezagholizadeh, Alan Do Omri, Ahmad Rashid

Abstract
Text generation with generative adversarial networks (GANs) can be divided into the text-based and code-based categories according to the type of signals used for discrimination. In this work, we introduce a novel text-based approach called Soft-GAN to effectively exploit GAN setup for text generation. We demonstrate how autoencoders (AEs) can be used for providing a continuous representation of sentences, which we will refer to as soft-text. This soft representation will be used in GAN discrimination to synthesize similar soft-texts. We also propose hybrid latent code and text-based GAN (LATEXT-GAN) approaches with one or more discriminators, in which a combination of the latent code and the soft-text is used for GAN discriminations. We perform a number of subjective and objective experiments on two well-known datasets (SNLI and Image COCO) to validate our techniques. We discuss the results using several evaluation metrics and show that the proposed techniques outperform the traditional GAN-based text-generation methods.
Anthology ID:
N19-1234
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2248–2258
Language:
URL:
https://aclanthology.org/N19-1234
DOI:
10.18653/v1/N19-1234
Bibkey:
Cite (ACL):
Md. Akmal Haidar, Mehdi Rezagholizadeh, Alan Do Omri, and Ahmad Rashid. 2019. Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2248–2258, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (Haidar et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/N19-1234.pdf
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