Non-Autoregressive machine Translation (NAT) models have demonstrated significant inference speedup but suffer from inferior translation accuracy. The common practice to tackle the problem is transferring the Autoregressive machine Translation (AT) knowledge to NAT models, e.g., with knowledge distillation. In this work, we hypothesize and empirically verify that AT and NAT encoders capture different linguistic properties and representations of source sentences. Therefore, we propose to adopt the multi-task learning to transfer the AT knowledge to NAT models through the encoder sharing. Specifically, we take the AT model as an auxiliary task to enhance NAT model performance. Experimental results on WMT14 English->German and WMT16 English->Romanian datasets show that the proposed multi-task NAT achieves significant improvements over the baseline NAT models.  In addition, experimental results demonstrate that our multi-task NAT is complementary to  the standard knowledge transfer method , knowledge distillation.Code is publicly available at URL
Non-Autoregressive machine Translation (NAT) models have demonstrated significant inference speedup but suffer from inferior translation accuracy. The common practice to tackle the problem is transferring the Autoregressive machine Translation (AT) knowledge to NAT models, e.g., with knowledge distillation. In this work, we hypothesize and empirically verify that AT and NAT encoders capture different linguistic properties of source sentences. Therefore, we propose to adopt Multi-Task learning to transfer the AT knowledge to NAT models through encoder sharing. Specifically, we take the AT model as an auxiliary task to enhance NAT model performance. Experimental results on WMT14 English-German and WMT16 English-Romanian datasets show that the proposed Multi-Task NAT achieves significant improvements over the baseline NAT models. Furthermore, the performance on large-scale WMT19 and WMT20 English-German datasets confirm the consistency of our proposed method. In addition, experimental results demonstrate that our Multi-Task NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.
D
and representations
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the multi-task
Multi-Task
D
the
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English->German
English-German
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English->Romanian
English-Romanian
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multi-task
Multi-Task
A
Furthermore, the performance on large-scale WMT19 and WMT20 English-German datasets confirm the consistency of our proposed method.
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multi-task
Multi-Task
A
knowledge distillation,
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, knowledge distillation.Code is publicly available at URL
for NAT.