Generative Adversarial Training Can Improve Neural Language Models

Autor:	Movahedi, Sajad, Shakery, Azadeh
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	While deep learning in the form of recurrent neural networks (RNNs) has caused a significant improvement in neural language modeling, the fact that they are extremely prone to overfitting is still a mainly unresolved issue. In this paper we propose a regularization method based on generative adversarial networks (GANs) and adversarial training (AT), that can prevent overfitting in neural language models. Unlike common adversarial training methods such as the fast gradient sign method (FGSM) that require a second back-propagation through time, and therefore effectively require at least twice the amount of time for regular training, the overhead of our method does not exceed more than 20% of the training of the baselines. Comment: An extended abstract selected for poster presentation at the Eastern European Machine Learning Summer School 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.09728 Zobrazit plný text záznamu View this record from Arxiv