Improving Non-Autoregressive Neural Machine Translation via Modeling Localness (2022.coling-1)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from poor localization quality due to sequential dependencies within the target sentence. |
| Approach: | They propose to introduce local information into NAT models by explicitly introducing local information about surrounding words into the encoder and decoder sides to achieve localness-aware representations. |
| Outcome: | The proposed method can achieve significant improvements over strong NAT baselines. |
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| Challenge: | Existing methods to capture local dependencies among output tokens are not efficient, causing errors of repeated translation. |
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| Challenge: | Non-autoregressive machine translation (NAT) has made great progress, but most studies focus on standard translation tasks. |
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