State Dep’t of Corr. v. Ludwick, 135 Nev. Adv. Op. 12 (May 2, 2019)

The Court determined that (1) a hearing officer must also give deference to the agency’s determination that a crime is so serious that termination serves the public good, even when the agency has no published regulation dictating that outcome, and (2) an administrative hearing officer committed a clear error of law in relying, in any way, upon an invalid regulation to review an agency’s determination to terminate for a first-time disciplinary action

TextGAIL: Generative Adversarial Imitation Learning for Text Generation

Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. Our approach uses contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task.Comment: AAAI 202