From Research to Production and Back: Ludicrously Fast Neural Machine Translation (D19-56)
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Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev
| Challenge: | Using the dominating submissions to the previous edition of the shared task, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models. |
| Approach: | They propose to use multi-agent dual-learning and noisy backward-forward translation to improve teacher-student training for Transformer-based student models. |
| Outcome: | The proposed model outperforms submissions to the previous edition of the WNGT efficiency shared task by 4 BLEU points and 10 BLUE points respectively. |
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Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch
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Monash University’s Submissions to the WNGT 2019 Document Translation Task (D19-56)
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| Challenge: | Despite the boom of work on document-level machine translation in the past two years, there has been a lack of the application of the proposed approaches to MT shared tasks. |
| Approach: | They propose to employ an established document-level neural machine translation model for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019). |
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| Challenge: | Existing knowledge demonstrates the superiority of TM-based neural machine translation only on TM specialized tasks . |
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Multi-split Reversible Transformers Can Enhance Neural Machine Translation (2021.eacl-main)
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| Challenge: | Large-scale transformers have been shown to improve neural machine translation performance but training these wider and deeper networks could be extremely memory intensive. |
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INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation (2023.acl-long)
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| Challenge: | Neural machine translation models induce a non-smooth representation space, which harms its generalization results. |
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| Challenge: | This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 . |
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Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019 (D19-56)
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| Challenge: | Neural sequence-to-sequence models are sensitive to architecture and hyperparameter settings. |
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