← Back to papers
2021 EMNLP EMNLP 2021

Tune in: The AFRL WMT21 News-Translation Systems

Abstract

AbstractThis paper describes the Air Force Research Laboratory (AFRL) machine translation sys- tems and the improvements that were developed during the WMT21 evaluation campaign. This year, we explore various methods of adapting our baseline models from WMT20 and again measure improvements in performance on the Russian–English language pair.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🐣 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

Grant Erdmann , Jeremy Gwinnup , Tim Anderson

Topics

Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Transfer Learning Deep Learning > Learning Types > Transfer Learning Machine Learning > Learning Types > Machine Translation

Keywords

neural machine translation model adaptation multilingual model news translation transformer model baseline model
Download PDF

Related papers

Continual Learning in Multilingual NMT via Language-Specific Embeddings 2021
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents 2021
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity 2021
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings 2021
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis 2021