Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation

Yui Oka; Katsuhito Sudoh; Satoshi Nakamura

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arXiv · 2107.13689

Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation

Yui Oka·Katsuhito Sudoh·Satoshi Nakamura

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Abstract

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.

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Yui Oka, Katsuhito Sudoh, Satoshi Nakamura. 2021-07-29. Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation. https://arxiv.org/abs/2107.13689

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