deepQuest-py: large and distilled models for quality estimation

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.382–389

Type

Conference contribution

Language

en

Description

© (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/

Collections

Research Institute in Information and Language Processing