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). |
| Outcome: | The proposed model achieves a BLEU score of 39.83 for En-De and 45.06 for De-En translation directions on the Rotowire test set. |
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| Challenge: | Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered. |
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University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task (D19-56)
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| Challenge: | University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with English and German as targeted languages. |
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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. |
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| Challenge: | In this paper, we explore multiway-models for Indian languages. |
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NTT Neural Machine Translation Systems at WAT 2019 (D19-52)
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| Challenge: | We submitted two systems for scientific paper subtask and timely disclosure subtask . we evaluated the usefulness of incorporating external data from a wide variety of web pages to improve the translation quality. |
| Approach: | They describe two different translation tasks submitted to WAT 2019 . they submitted scientific paper subtasks and timely disclosure subtask . |
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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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UCSYNLP-Lab Machine Translation Systems for WAT 2019 (D19-52)
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| Approach: | They describe the UCSYNLP-Lab submission to WAT 2019 for Myanmar-English translation tasks in both directions. |
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| Challenge: | Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. |
| Approach: | They empirically test neural machine translation on a Chinese–English news translation task . they show human raters prefer human over machine translation when evaluating documents . |
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Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation (L18-1)
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| Challenge: | Currently, there is no widely accepted standard for evaluation of machine translation (MT) for Chinese-to-English translation, there are no standard for standardized training sets, development sets, and test sets. |
| Approach: | They propose to use Chinese-to-English machine translation as a benchmark . they build a highly competitive state-of-the-art MT system that outperforms reported results . |
| Outcome: | The proposed system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years. |