Papers with WMT16 En-Ro
Synchronous Syntactic Attention for Transformer Neural Machine Translation (2021.acl-srw)
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| Challenge: | Existing syntaxbased NMT models use monolingual syntactic information on either side or both. |
| Approach: | They propose a mechanism that synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target- and target side self- attentions mapped by the encoder-decoder attention matrix. |
| Outcome: | The proposed method improves translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU). |
Weight Distillation: Transferring the Knowledge in Neural Network Parameters (2021.acl-long)
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| Challenge: | Knowledge distillation is an effective method for model acceleration and compression. |
| Approach: | They propose to use parameters to distill knowledge from large neural networks to small ones . they propose to do this by using a parameter generator to transfer the knowledge to a small neural network . |
| Outcome: | The proposed method learns a small network 1.88 2.94x faster than the large network but with competitive BLEU points. |
Improving Non-autoregressive Neural Machine Translation with Monolingual Data (2020.acl-main)
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| Challenge: | Neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. |
| Approach: | They leverage large monolingual corpora to improve the NAR model's performance by transferring the autoregressive model' s generalization ability while preventing overfitting. |
| Outcome: | The proposed methods on the WMT14 En-De and WMT16 En-Ro news translation tasks show that monolingual data augmentation improves the NAR model to approach the teacher AR model’s performance. |