1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2020★ 1 cited
Distributional Discrepancy: A Metric for Unconditional Text Generation
Ping Cai, Xingyuan Chen, Peng Jin +2
The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However,…
cs.CV2020
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
Xingyuan Chen, Ping Cai, Peng Jin +3
The autoregressive language model (ALM) trained with maximum likelihood estimation (MLE) is widely used in unconditional text generation. Due to exposure bias, the generated texts…
cs.CV2019
The Detection of Distributional Discrepancy for Text Generation
Xingyuan Chen, Ping Cai, Peng Jin +4
The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to allev…