OpenKiwi: An Open Source Framework for Quality Estimation
Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, André F. T. Martins
Abstract
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.- Anthology ID:
- P19-3020
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 117–122
- Language:
- URL:
- https://aclanthology.org/P19-3020
- DOI:
- 10.18653/v1/P19-3020
- Award:
- Best Demo Paper
- Cite (ACL):
- Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, and André F. T. Martins. 2019. OpenKiwi: An Open Source Framework for Quality Estimation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 117–122, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- OpenKiwi: An Open Source Framework for Quality Estimation (Kepler et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-3020.pdf
- Code
- Unbabel/OpenKiwi