IST-Unbabel 2021 Submission for the Quality Estimation Shared Task
Zerva, C
; Stigt, D.
;
Rei , R.
; Farinha, A. C.
; Ramos, P. G.
; de Souza, J. G.
;
Glushkova, T.
; Vera, M.
; Kepler, F.
;
Martins, A.
IST-Unbabel 2021 Submission for the Quality Estimation Shared Task, Proc Conference on Machine Translation WMT, Conference Online, Vol. , pp. - , November, 2021.
Digital Object Identifier:
Download Full text PDF ( 339 KBs)
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.
