EMNLP 2021system demonstrations1 citations
deepQuest-py: Large and Distilled Models for Quality Estimation
Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia
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.
BibTeX
@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"
}