2021
EMNLP
EMNLP 2021
IST-Unbabel 2021 Submission for the Quality Estimation Shared Task
Abstract
AbstractWe present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation. Our team participated on two tasks: Direct Assessment and Post-Editing Effort, encompassing a total of 35 submissions. For all submissions, our efforts focused on training multilingual models on top of OpenKiwi predictor-estimator architecture, using pre-trained multilingual encoders combined with adapters. We further experiment with and uncertainty-related objectives and features as well as training on out-of-domain direct assessment data.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— uncertainty objective
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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 > Multi-Task Learning
Deep Learning > Models > Transformers
Deep Learning > Techniques > Transfer Learning
Machine Learning > Learning Types > Multi-Lingual Learning