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2019 ACL ACL 2019

NICT’s Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task

Abstract

AbstractThis paper presents the NICT’s participation in the WMT19 unsupervised news translation task. We participated in the unsupervised translation direction: German-Czech. Our primary submission to the task is the result of a simple combination of our unsupervised neural and statistical machine translation systems. Our system is ranked first for the German-to-Czech translation task, using only the data provided by the organizers (“constraint’”), according to both BLEU-cased and human evaluation. We also performed contrastive experiments with other language pairs, namely, English-Gujarati and English-Kazakh, to better assess the effectiveness of unsupervised machine translation in for distant language pairs and in truly low-resource conditions.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Natural Language Processing, Speech & Audio

Authors

Benjamin Marie , Haipeng Sun , Rui Wang , Kehai Chen , Atsushi Fujita , Masao Utiyama , Eiichiro Sumita

Topics

Machine Learning > Learning Types > Unsupervised Learning Natural Language Processing > Applications > Machine Translation Natural Language Processing > Generation > Machine Translation Deep Learning > Learning Types > Deep Learning Deep Learning > Learning Types > Unsupervised Learning

Keywords

neural machine translation system combination statistical machine translation low-resource language unsupervised machine translation language pair
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