@inproceedings{kepler-etal-2019-unbabels,
title = "Unbabel{'}s Participation in the {WMT}19 Translation Quality Estimation Shared Task",
author = "Kepler, Fabio and
Tr{\'e}nous, Jonay and
Treviso, Marcos and
Vera, Miguel and
G{\'o}is, Ant{\'o}nio and
Farajian, M. Amin and
Lopes, Ant{\'o}nio V. and
Martins, Andr{\'e} F. T.",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5406",
doi = "10.18653/v1/W19-5406",
pages = "78--84",
abstract = "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.",
}
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%0 Conference Proceedings
%T Unbabel’s Participation in the WMT19 Translation Quality Estimation Shared Task
%A Kepler, Fabio
%A Trénous, Jonay
%A Treviso, Marcos
%A Vera, Miguel
%A Góis, António
%A Farajian, M. Amin
%A Lopes, António V.
%A Martins, André F. T.
%S Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F kepler-etal-2019-unbabels
%X 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.
%R 10.18653/v1/W19-5406
%U https://aclanthology.org/W19-5406
%U https://doi.org/10.18653/v1/W19-5406
%P 78-84
Markdown (Informal)
[Unbabel’s Participation in the WMT19 Translation Quality Estimation Shared Task](https://aclanthology.org/W19-5406) (Kepler et al., 2019)
ACL
- Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, António Góis, M. Amin Farajian, António V. Lopes, and André F. T. Martins. 2019. Unbabel’s Participation in the WMT19 Translation Quality Estimation Shared Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 78–84, Florence, Italy. Association for Computational Linguistics.