2021
EMNLP
EMNLP 2021
Adapting Neural Machine Translation for Automatic Post-Editing
Abstract
AbstractAutomatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT’21 Automatic Post-Editing (APE) English-German (En-De) shared task. We leverage the state-of-the-art MT system (Ng et al., 2019) for this task. For further improvements, we adapt the MT model to the task domain by using WikiMatrix (Schwenket al., 2021) followed by fine-tuning with additional APE samples from previous editions of the shared task (WMT-16,17,18) and ensembling the models. Our systems beat the baseline on TER scores on the WMT’21 test set.
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Interdisciplinary Bridge
— Deep Learning and Machine 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, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Machine Translation
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Generation > Machine Translation
Deep Learning > Learning Types > Deep Learning
Deep Learning > Models > Language Models