deepQuest-py: large and distilled models for quality estimation

Alva-Manchego, Fernando, Obamuyide, Abiola, Gajbhiye, Amit, Blain, Frédéric, Fomicheva, Marina and Specia, Lucia 2021. deepQuest-py: large and distilled models for quality estimation. Presented at: 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Punta Cana, Dominican Republic, 7-11 November 2021. Published in: Adel, Heike and Shi, Shuming eds. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, pp. 382-389. 10.18653/v1/2021.emnlp-demo.42

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Abstract

We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	File distributed under a Creative Commons Attribution 4.0 International License.
Publisher:	Association for Computational Linguistics
Date of First Compliant Deposit:	14 February 2022
Last Modified:	14 Feb 2022 16:30
URI:	https://orca.cardiff.ac.uk/id/eprint/147257

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