Non-autoregressive approaches purpose to enhance the inference pace of translation fashions by solely requiring a single ahead go to generate the output sequence as an alternative of iteratively producing every predicted token. Consequently, their translation high quality nonetheless tends to be inferior to their autoregressive counterparts as a result of a number of points involving output token interdependence. On this work, we take a step again and revisit a number of methods which were proposed for bettering non-autoregressive translation fashions and examine their mixed translation high quality and pace implications underneath third-party testing environments. We offer novel insights for establishing robust baselines utilizing size prediction or CTC-based structure variants and contribute standardized BLEU, chrF++, and TER scores utilizing sacreBLEU on 4 translation duties, which crucially have been lacking as inconsistencies in the usage of tokenized BLEU result in deviations of as much as 1.7 BLEU factors. Our open-sourced code is built-in into fairseq for reproducibility.