  Non-autoregressive Transformer is a promising text generation model. However,
current non-autoregressive models still fall behind their autoregressive
counterparts in translation quality. We attribute this accuracy gap to the lack
of dependency modeling among decoder inputs. In this paper, we propose CNAT,
which learns implicitly categorical codes as latent variables into the
non-autoregressive decoding. The interaction among these categorical codes
remedies the missing dependencies and improves the model capacity. Experiment
results show that our model achieves comparable or better performance in
machine translation tasks, compared with several strong baselines.
