We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation. We participated on the word, sentence, and document-level tracks, encompassing 3 language pairs: English-German, English-Russian, and English-French. Our submissions build upon the recent OpenKiwi framework: we combine linear, neural, and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models. We compare systems individually and propose new ensemble techniques for word and sentence-level predictions. We also propose a simple technique for converting word labels into document-level predictions. Overall, our submitted systems achieve the best results on all tracks and language pairs by a considerable margin.
@article{arxiv.1907.10352,
title = {Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task},
author = {Fabio Kepler and Jonay Trénous and Marcos Treviso and Miguel Vera and António Góis and M. Amin Farajian and António V. Lopes and André F. T. Martins},
journal= {arXiv preprint arXiv:1907.10352},
year = {2019}
}
Comments
In Proceedings of the Fourth Conference on Machine Translation (WMT) 2019: https://www.aclweb.org/anthology/W19-5406/