← Back to papers
2019 ACL ACL 2019

The AFRL WMT19 Systems: Old Favorites and New Tricks

Abstract

AbstractThis paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign. This year, we refine our approach to training popular neural machine translation toolkits, experiment with a new domain adaptation technique and again measure improvements in performance on the Russian–English language pair.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — russian-english translation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🐣 Hot Topic Early Bird — model ensemble

Authors

Jeremy Gwinnup , Grant Erdmann , Tim Anderson

Topics

Machine Learning > Application Areas > Domain Adaptation Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Natural Language Processing > Generation > Machine Translation Machine Learning > Learning Types > Domain Adaptation Deep Learning > Learning Types > Transfer Learning

Keywords

domain adaptation neural machine translation model ensemble data filtering russian-english translation neural machine translation toolkit
Download PDF

Related papers

What do phone embeddings learn about Phonology? 2019
Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages 2019
Understanding Undesirable Word Embedding Associations 2019
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text 2019
Domain Adaptation of Neural Machine Translation by Lexicon Induction 2019