deepQuest-py: Large and distilled models for quality estimation

Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frederic Blain, Marina Fomicheva, Lucia Specia

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages382-389
Number of pages8
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Publication series

NameEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
Country/TerritoryDominican Republic
CityVirtual, Punta Cana
Period7/11/2111/11/21

Keywords

  • Quality Estimation
  • Machine Translation

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