We 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.
@article{arxiv.1902.08646,
title = {OpenKiwi: An Open Source Framework for Quality Estimation},
author = {Fábio Kepler and Jonay Trénous and Marcos Treviso and Miguel Vera and André F. T. Martins},
journal= {arXiv preprint arXiv:1902.08646},
year = {2019}
}
Comments
Published at the Annual Meeting of the Association for Computational Linguistics (ACL) 2019: System Demonstrations (https://aclweb.org/anthology/papers/P/P19/P19-3020/)