MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation (2022.findings-acl)
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| Challenge: | Non-autoregressive neural machine translation models remove dependency between tokens in the target sentence and generate all tokens on parallel . |
| Approach: | They propose a non-autoregressive neural machine translation model that decodes with the Mask-Predict algorithm which iteratively refines the output. |
| Outcome: | The proposed algorithm increases the performance of the WMT’14 translation task by 1.39 points. |
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| Challenge: | a masked language model is used to train a model to predict subsets of mangled words . a parallel decoding algorithm can be used to generate translations in a constant number of iterations. |
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