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2019 EMNLP EMNLP 2019

SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task

Abstract

AbstractThis paper describes Systran’s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — russian japanese translation
🐣 Hot Topic Early Bird — synthetic data generation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jitao Xu , TuAnh Nguyen , MinhQuang Pham , Josep Crego , Jean Senellart

Topics

Machine Learning > Application Areas > Data Augmentation Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Natural Language Processing > Generation > Machine Translation Machine Learning > Learning Types > Data Augmentation

Keywords

transformer architecture data augmentation neural machine translation synthetic data generation low-resource translation russian japanese translation
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