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2020 EMNLP EMNLP 2020

GTCOM Neural Machine Translation Systems for WMT20

Abstract

AbstractThis paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and Pashto) to English. Further, we get the best BLEU scores in the directions of English to Pashto, Pashto to English and Khmer to English (13.1, 23.1 and 25.5 respectively) among all the participants. Our submitted systems are unconstrained and focus on mBART (Multilingual Bidirectional and Auto-Regressive Transformers), back-translation and forward-translation. Also, we apply rules, language model and RoBERTa model to filter monolingual, parallel sentences and synthetic sentences. Besides, we validate the difference of the vocabulary built from monolingual data and parallel data.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — multilingual bidirectional auto-regressive transformer
🐣 Hot Topic Early Bird — multilingual transformer
🐝 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

Chao Bei , Hao Zong , Qingmin Liu , Conghu Yuan

Topics

Machine Learning > Learning Types > Self-Supervised Learning Deep Learning > Architectures > Transformers Deep Learning > Models > Diffusion Models Natural Language Processing > Applications > Machine Translation Natural Language Processing > Generation > Machine Translation Deep Learning > Models > Transformers Deep Learning > Learning Types > Machine Translation

Keywords

neural machine translation language model multilingual transformer multilingual bidirectional auto-regressive transformer
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