Papers by Jonathan Sauder
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation (2020.lrec-1)
Copied to clipboard
| Challenge: | Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) however, they are reportedly suffering from training instability and mode collapse, and therefore outperform conventional MLE models. |
| Approach: | They propose a method to improve Generative Adversarial Nets (GANs) using best student forcing and discriminators to increase training stability and sample diversity. |
| Outcome: | The proposed techniques outperform MLE models and outperformed existing approaches in terms of sample diversity and training stability. |