Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task
; Farinha, A. C.
; Stigt, D.
; Stuart, C.
; Ramos, P. G.
; 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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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.