A Discriminator Improves Unconditional Text Generation without Updating the Generator

We propose a novel mechanism to improve an unconditional text generator with a discriminator, which is trained to estimate the probability that a sample comes from real or generated data. In contrast to recent discrete language generative adversarial networks (GAN) which update the parameters of the generator directly, our method only retains generated samples which are determined to come from real data with relatively high probability by the discriminator. This not only detects valuable information, but also avoids the mode collapse introduced by GAN. To the best of our knowledge, this is the first method which improves the neural language models (LM) trained with maximum likelihood estimation (MLE) by using a discriminator as a filter. Experimental results show that our mechanism improves both RNN-based and Transformer-based LMs when measuring in sample quality and sample diversity simultaneously at different softmax temperatures (a previously noted deficit of language GANs). Further, by recursively adding more discriminators, more powerful generators are created.