IST-Unbabel 2021 Submission for the Quality Estimation Shared Task
Chrysoula Zerva, Daan van Stigt, Ricardo Rei, Ana C Farinha, Pedro Ramos, José G. C. de Souza, Taisiya Glushkova, Miguel Vera, Fabio Kepler, André F. T. Martins
Abstract
We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation. Our team participated on two tasks: Direct Assessment and Post-Editing Effort, encompassing a total of 35 submissions. For all submissions, our efforts focused on training multilingual models on top of OpenKiwi predictor-estimator architecture, using pre-trained multilingual encoders combined with adapters. We further experiment with and uncertainty-related objectives and features as well as training on out-of-domain direct assessment data.- Anthology ID:
- 2021.wmt-1.102
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 961–972
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.102
- DOI:
- Cite (ACL):
- Chrysoula Zerva, Daan van Stigt, Ricardo Rei, Ana C Farinha, Pedro Ramos, José G. C. de Souza, Taisiya Glushkova, Miguel Vera, Fabio Kepler, and André F. T. Martins. 2021. IST-Unbabel 2021 Submission for the Quality Estimation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 961–972, Online. Association for Computational Linguistics.
- Cite (Informal):
- IST-Unbabel 2021 Submission for the Quality Estimation Shared Task (Zerva et al., WMT 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.wmt-1.102.pdf