157 citations · 279 across the 8 of their papers we have counts for
4 papers · 1 filter
Contextualized Perturbation for Textual Adversarial Attack
Dianqi Li, Yizhe Zhang, Hao Peng +4
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of genera…
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Kevin Lin, Ming-Yu Liu, Ming-Ting Sun +1
Text style transfer refers to the task of rephrasing a given text in a different style. While various methods have been proposed to advance the state of the art, they often assume…
Domain Adaptive Text Style Transfer
Dianqi Li, Yizhe Zhang, Zhe Gan +4
Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In t…
Adversarial Ranking for Language Generation
Kevin Lin, Dianqi Li, Xiaodong He +2
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 th…