  Non-autoregressive models are promising on various text generation tasks.
Previous work hardly considers to explicitly model the positions of generated
words. However, position modeling is an essential problem in non-autoregressive
text generation. In this study, we propose PNAT, which incorporates positions
as a latent variable into the text generative process. Experimental results
show that PNAT achieves top results on machine translation and paraphrase
generation tasks, outperforming several strong baselines.
