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We present a non-autoregressive system submission to the WMT 22 EfficientTranslation Shared Task. Our system was used by Helcl et al. (2022) in anattempt to provide fair comparison between non-autoregressive andautoregressive models. This submission is an effort to establish solidbaselines along with sound evaluation methodology, particularly in terms ofmeasuring the decoding speed. The model itself is a 12-layer Transformer modeltrained with connectionist temporal classification on knowledge-distilleddataset by a strong autoregressive teacher model.

CUNI Non-Autoregressive System for the WMT 22 Efficient Translation Shared Task