Pedro Ramos


2021

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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.

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Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task
Ricardo Rei | Ana C Farinha | Chrysoula Zerva | Daan van Stigt | Craig Stewart | Pedro Ramos | Taisiya Glushkova | André F. T. Martins | Alon Lavie
Proceedings of the Sixth Conference on Machine Translation

In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics Shared Task. With this year’s focus on Multidimensional Quality Metric (MQM) as the ground-truth human assessment, our aim was to steer COMET towards higher correlations with MQM. We do so by first pre-training on Direct Assessments and then fine-tuning on z-normalized MQM scores. In our experiments we also show that reference-free COMET models are becoming competitive with reference-based models, even outperforming the best COMET model from 2020 on this year’s development data. Additionally, we present COMETinho, a lightweight COMET model that is 19x faster on CPU than the original model, while also achieving state-of-the-art correlations with MQM. Finally, in the “QE as a metric” track, we also participated with a QE model trained using the OpenKiwi framework leveraging MQM scores and word-level annotations.