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2021 EMNLP EMNLP 2021

Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021

Abstract

AbstractWe present our development of the multilingual machine translation system for the large-scale multilingual machine translation task at WMT 2021. Starting form the provided baseline system, we investigated several techniques to improve the translation quality on the target subset of languages. We were able to significantly improve the translation quality by adapting the system towards the target subset of languages and by generating synthetic data using the initial model. Techniques successfully applied in zero-shot multilingual machine translation (e.g. similarity regularizer) only had a minor effect on the final translation performance.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — similarity regularizer
🐝 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

Danni Liu , Jan Niehues

Topics

Machine Learning > Learning Types > Zero-Shot Learning Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Transfer Learning Natural Language Processing > Generation > Machine Translation Machine Learning > Learning Types > Machine Translation

Keywords

machine translation multilingual translation multilingual machine translation synthetic data generation synthetic datum zero-shot translation language adaptation similarity regularizer
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