Authors
Alva-Manchego, FernandoObamuyide, Abiola
Gajbhiye, Amit
Blain, Frederic
Fomicheva, Marina
Specia, Lucia
Editors
Adel, HeikeShi, Shuming
Issue Date
2021-11-01
Metadata
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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.Citation
Alva-Manchego, F., Obamuyide, A., Gajbhiye, A., Blain, F., Fomicheva, M. and Specia, L. (2021) deepQuest-py: large and distilled models for quality estimation. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations: Association for Computational Linguistics, pp.382–389Additional Links
https://aclanthology.org/2021.emnlp-demo.42/Type
Conference contributionLanguage
enDescription
© (2021) The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2021.emnlp-demo.42/ae974a485f413a2113503eed53cd6c53
10.18653/v1/2021.emnlp-demo.42
Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/