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2019 ACL ACL 2019

OpenKiwi: An Open Source Framework for Quality Estimation

Abstract

AbstractWe introduce OpenKiwi, a Pytorch-based open source framework for translation quality estimation. OpenKiwi supports training and testing of word-level and sentence-level quality estimation systems, implementing the winning systems of the WMT 2015–18 quality estimation campaigns. We benchmark OpenKiwi on two datasets from WMT 2018 (English-German SMT and NMT), yielding state-of-the-art performance on the word-level tasks and near state-of-the-art in the sentence-level tasks.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
📈 Trend Setter — Quality Estimation
🧭 Keyword Pioneer — pytorch framework
🐝 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

Fabio Kepler , Jonay Trénous , Marcos Treviso , Miguel Vera , André F. T. Martins

Topics

Natural Language Processing > Applications > Machine Translation Natural Language Processing > Generation > Machine Translation Machine Learning > Learning Types > Classification Deep Learning > Models > Neural Networks Natural Language Processing > Applications > Quality Estimation

Keywords

machine translation neural machine translation quality estimation statistical machine translation sentence-level prediction word-level prediction neural network pytorch framework wmt benchmark
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