Miguel Vera


2021

pdf
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
Proceedings of the Sixth Conference on Machine Translation

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.

2020

pdf
IST-Unbabel Participation in the WMT20 Quality Estimation Shared Task
João Moura | Miguel Vera | Daan van Stigt | Fabio Kepler | André F. T. Martins
Proceedings of the Fifth Conference on Machine Translation

We present the joint contribution of IST and Unbabel to the WMT 2020 Shared Task on Quality Estimation. Our team participated on all tracks (Direct Assessment, Post-Editing Effort, Document-Level), encompassing a total of 14 submissions. Our submitted systems were developed by extending the OpenKiwi framework to a transformer-based predictor-estimator architecture, and to cope with glass-box, uncertainty-based features coming from neural machine translation systems.

2019

pdf
OpenKiwi: An Open Source Framework for Quality Estimation
Fabio Kepler | Jonay Trénous | Marcos Treviso | Miguel Vera | André F. T. Martins
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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.

pdf
Unbabel’s Participation in the WMT19 Translation Quality Estimation Shared Task
Fabio Kepler | Jonay Trénous | Marcos Treviso | Miguel Vera | António Góis | M. Amin Farajian | António V. Lopes | André F. T. Martins
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

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.