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2018 EMNLP EMNLP 2018

CUNI Submissions in WMT18

Abstract

AbstractWe participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a “parent model” for the high-resource language pair followed by adaptation on the related low-resource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — hybrid phrase-based
🐣 Hot Topic Early Bird — model adaptation
🐝 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

Tom Kocmi , Roman Sudarikov , Ondřej Bojar

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Paradigms > Transfer Learning Natural Language Processing > Generation > Machine Translation Deep Learning > Models > Transformers Deep Learning > Learning Types > Transfer Learning

Keywords

transformer architecture transfer learning neural machine translation model adaptation low-resource language parent model hybrid phrase-based
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