@inproceedings{lin2017adversarial,
author = {Lin, Kevin and Li, Dianqi and He, Xiaodong and Zhang, Zhengyou and Sun, Ming-Ting},
title = {Adversarial Ranking for Language Generation},
booktitle = {NIPS 2017},
year = {2017},
month = {December},
abstract = {Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than train the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.},
publisher = {NIPS},
url = {http://approjects.co.za/?big=en-us/research/publication/adversarial-ranking-language-generation/},
edition = {NIPS 2017},
}