deepQuest-py: Large and Distilled Models for Quality Estimation

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Abstract

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. deepQuest-py is available at https://github.com/sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.

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APA

Alva-Manchego, F., Obamuyide, A., Gajbhiye, A., Blain, F., Fomicheva, M., & Specia, L. (2021). deepQuest-py: Large and Distilled Models for Quality Estimation. In EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 382–389). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.emnlp-demo.42

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