← Back to papers
2020 EMNLP EMNLP 2020

IST-Unbabel Participation in the WMT20 Quality Estimation Shared Task

Abstract

AbstractWe present the joint contribution of IST and Unbabel to the WMT 2020 Shared Task on Quality Estimation. Our team participated on all tracks (Direct Assessment, Post-Editing Effort, Document-Level), encompassing a total of 14 submissions. Our submitted systems were developed by extending the OpenKiwi framework to a transformer-based predictor-estimator architecture, and to cope with glass-box, uncertainty-based features coming from neural machine translation systems.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — predictor-estimator architecture
🐝 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

João Moura , Miguel Vera , Daan van Stigt , Fabio Kepler , André F. T. Martins

Topics

Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Artificial Intelligence > Core AI > Natural Language Processing Machine Learning > Learning Types > Machine Translation

Keywords

neural machine translation quality estimation uncertainty estimation transformer model predictor-estimator architecture post-editing effort uncertainty-based feature glass-box feature
Download PDF

Related papers

Fast semantic parsing with well-typedness guarantees 2020
Detecting Objectifying Language in Online Professor Reviews 2020
Analogous Process Structure Induction for Sub-event Sequence Prediction 2020
Aspect Sentiment Classification with Aspect-Specific Opinion Spans 2020
Robust and Interpretable Grounding of Spatial References with Relation Networks 2020