2022
EMNLP
EMNLP 2022
GTCOM Neural Machine Translation Systems for WMT22
Abstract
AbstractGTCOM participates in five directions: English to/from Ukrainian, Ukrainian to/from Czech, English to Chinese and English to Croatian. Our submitted systems are unconstrained and focus on backtranslation, multilingual translation model and finetuning. Multilingual translation model focus on X to one and one to X. We also apply rules and language model to filter monolingual, parallel sentences and synthetic sentences.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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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, Security & Privacy, Speech & Audio