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Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task

Rei, R. ; Farinha, A. C. ; Zerva, C ; Stigt, D. ; Stuart, C. ; Ramos, P. G. ; Glushkova, T. ; Martins, A. ; Lavie, A.

Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task, Proc Conference on Machine Translation WMT, Conference Online, Vol. , pp. - , November, 2021.

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Abstract
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 light-weight 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.