@inproceedings{644c75b60db0454f9817bc9161004a3e,
title = "Evaluating text gans as language models",
abstract = "Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of “exposure bias”. However, A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric. In this work, we propose to approximate the distribution of text generated by a GAN, which permits evaluating them with traditional probability-based LM metrics. We apply our approximation procedure on several GAN-based models and show that they currently perform substantially worse than state-of-the-art LMs. Our evaluation procedure promotes better understanding of the relation between GANs and LMs, and can accelerate progress in GAN-based text generation.",
author = "Guy Tevet and Gavriel Habib and Vered Shwartz and Jonathan Berant",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computational Linguistics; 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
year = "2019",
language = "الإنجليزيّة",
series = "NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "2241--2247",
booktitle = "Long and Short Papers",
address = "الولايات المتّحدة",
}