@inproceedings{budhkar-etal-2019-generative,
title = "Generative Adversarial Networks for Text Using Word2vec Intermediaries",
author = "Budhkar, Akshay and
Vishnubhotla, Krishnapriya and
Hossain, Safwan and
Rudzicz, Frank",
editor = "Augenstein, Isabelle and
Gella, Spandana and
Ruder, Sebastian and
Kann, Katharina and
Can, Burcu and
Welbl, Johannes and
Conneau, Alexis and
Ren, Xiang and
Rei, Marek",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4303",
doi = "10.18653/v1/W19-4303",
pages = "15--26",
abstract = "Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images. In this work, we apply similar techniques for the generation of text. We propose a novel approach to handle the discrete nature of text, during training, using word embeddings. Our method is agnostic to vocabulary size and achieves competitive results relative to methods with various discrete gradient estimators.",
}
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%0 Conference Proceedings
%T Generative Adversarial Networks for Text Using Word2vec Intermediaries
%A Budhkar, Akshay
%A Vishnubhotla, Krishnapriya
%A Hossain, Safwan
%A Rudzicz, Frank
%Y Augenstein, Isabelle
%Y Gella, Spandana
%Y Ruder, Sebastian
%Y Kann, Katharina
%Y Can, Burcu
%Y Welbl, Johannes
%Y Conneau, Alexis
%Y Ren, Xiang
%Y Rei, Marek
%S Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F budhkar-etal-2019-generative
%X Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images. In this work, we apply similar techniques for the generation of text. We propose a novel approach to handle the discrete nature of text, during training, using word embeddings. Our method is agnostic to vocabulary size and achieves competitive results relative to methods with various discrete gradient estimators.
%R 10.18653/v1/W19-4303
%U https://aclanthology.org/W19-4303
%U https://doi.org/10.18653/v1/W19-4303
%P 15-26
Markdown (Informal)
[Generative Adversarial Networks for Text Using Word2vec Intermediaries](https://aclanthology.org/W19-4303) (Budhkar et al., RepL4NLP 2019)
ACL