@inproceedings{8122e3c52f4047149df364b3b7d3f4a0,
title = "deepQuest-py: Large and distilled models for quality estimation",
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. deepQuestpy is available at https://github.com/ sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.",
keywords = "Quality Estimation, Machine Translation",
author = "Fernando Alva-Manchego and Abiola Obamuyide and Amit Gajbhiye and Frederic Blain and Marina Fomicheva and Lucia Specia",
year = "2021",
doi = "10.18653/v1/2021.emnlp-demo.42",
language = "English",
series = "EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
publisher = "Association for Computational Linguistics",
pages = "382--389",
booktitle = "EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing",
note = "2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 ; Conference date: 07-11-2021 Through 11-11-2021",
}