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deepQuest-py: large and distilled models for quality estimationAlva-Manchego, Fernando; Obamuyide, Abiola; Gajbhiye, Amit; Blain, Frederic; Fomicheva, Marina; Specia, Lucia (Association for Computational Linguistics, 2021-12-31)We introduce deepQuest-py, a framework for training and evaluation of large and lightweight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-ofthe-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions. deepQuestpy is available at https://github.com/ sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.
Knowledge distillation for quality estimationGajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando; Blain, Frederic; Obamuyide, Abiola; Aletras, Nikolaos; Specia, Lucia (Association for Computational Linguistics, 2021-08-01)Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations. Recent success in QE stems from the use of multilingual pre-trained representations, where very large models lead to impressive results. However, the inference time, disk and memory requirements of such models do not allow for wide usage in the real world. Models trained on distilled pre-trained representations remain prohibitively large for many usage scenarios. We instead propose to directly transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. We show that this approach, in combination with data augmentation, leads to light-weight QE models that perform competitively with distilled pre-trained representations with 8x fewer parameters.