@inproceedings{alva-manchego-etal-2021-deepquest,
title = "deep{Q}uest-py: {L}arge and Distilled Models for Quality Estimation",
author = "Alva-Manchego, Fernando and
Obamuyide, Abiola and
Gajbhiye, Amit and
Blain, Fr{\'e}d{\'e}ric and
Fomicheva, Marina and
Specia, Lucia",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.42",
doi = "10.18653/v1/2021.emnlp-demo.42",
pages = "382--389",
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 \url{https://github.com/sheffieldnlp/deepQuest-py} under a CC BY-NC-SA licence.",
}
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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.</abstract>
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%0 Conference Proceedings
%T deepQuest-py: Large and Distilled Models for Quality Estimation
%A Alva-Manchego, Fernando
%A Obamuyide, Abiola
%A Gajbhiye, Amit
%A Blain, Frédéric
%A Fomicheva, Marina
%A Specia, Lucia
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F alva-manchego-etal-2021-deepquest
%X 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.
%R 10.18653/v1/2021.emnlp-demo.42
%U https://aclanthology.org/2021.emnlp-demo.42
%U https://doi.org/10.18653/v1/2021.emnlp-demo.42
%P 382-389
Markdown (Informal)
[deepQuest-py: Large and Distilled Models for Quality Estimation](https://aclanthology.org/2021.emnlp-demo.42) (Alva-Manchego et al., EMNLP 2021)
ACL
- Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, and Lucia Specia. 2021. deepQuest-py: Large and Distilled Models for Quality Estimation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 382–389, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.