| Challenge: | Existing approaches to improve neural machine translation models with multiple decoding passes lack proper policies to terminate multi-pass processes. |
| Approach: | They propose a novel architecture of Rewriter-Evaluator to terminate multi-pass decoding . they propose prioritized gradient descent to jointly and efficiently train rewriter and evaluator . |
| Outcome: | The proposed architecture significantly outperforms existing methods on three translation tasks and reduces performance gaps to oracle policies. |
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| Challenge: | End-to-end neural machine translation (NMT) has attracted increasing attention in recent years. |
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
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| Challenge: | Existing non-autoregressive neural machine translations have poor inference speed but weak recognition of erroneous translation pieces. |
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| Challenge: | Neural Machine Translation models still require translation post-editing to rectify errors and enhance quality under critical settings. |
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Quality-Aware Decoding for Neural Machine Translation (2022.naacl-main)
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Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, Andre Martins
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