Using Perturbed Length aware Positional Encoding for Non autoregressive Neural Machine Translation
Non autoregressive neural machine translation (NAT) usually employs sequence level knowledge distillation using autoregressive neural machine translation (AT) as its teacher model. However, a NAT model often outputs shorter sentences than an AT model. In this work, we propose sequence level knowledge distillation (SKD) using perturbed length aware positional encoding and apply it to a student model, the Levenshtein Transformer. Our method outperformed a standard Levenshtein Transformer by 2.5 points in bilingual evaluation understudy (BLEU) at maximum in a WMT14 German to English translation. The NAT model output longer sentences than the baseline NAT models.
ArXiv