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2019 ACL ACL 2019

UdS Submission for the WMT 19 Automatic Post-Editing Task

Abstract

AbstractIn this paper, we describe our submission to the English-German APE shared task at WMT 2019. We utilize and adapt an NMT architecture originally developed for exploiting context information to APE, implement this in our own transformer model and explore joint training of the APE task with a de-noising encoder.

🌉 Interdisciplinary Bridge — Deep Learning and Natural Language Processing
🧭 Keyword Pioneer — denoising encoder
🐣 Hot Topic Early Bird — joint training
🐝 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

Authors

Hongfei Xu , Qiuhui Liu , Josef van Genabith

Topics

Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Deep Learning > Models > Transformers Deep Learning > Learning Types > Transfer Learning

Keywords

neural machine translation automatic post-editing joint training context information context encoding transformer model denoising encoder
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