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Text Generation with Deep Variational GAN

2021-04-27 21:42:13
Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung

Abstract

Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence generation tasks. However, the issue of mode-collapsing remains a main issue for the current models. In this paper we propose a GAN-based generic framework to address the problem of mode-collapse in a principled approach. We change the standard GAN objective to maximize a variational lower-bound of the log-likelihood while minimizing the Jensen-Shanon divergence between data and model distributions. We experiment our model with text generation task and show that it can generate realistic text with high diversity.

Abstract (translated)

URL

https://arxiv.org/abs/2104.13488

PDF

https://arxiv.org/pdf/2104.13488.pdf


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