Specia, L. orcid.org/0000-0002-5495-3128, Shah, K., de Souza, J.G.C. et al. (1 more author) (2013) QuEst - A translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 04-09 Aug 2013, Sofia, Bulgaria. Association for Computational Linguistics (ACL) , pp. 79-84.
Abstract
We describe QUEST, an open source framework for machine translation quality estimation. The framework allows the extraction of several quality indicators from source segments, their translations, external resources (corpora, language models, topic models, etc.), as well as language tools (parsers, part-of-speech tags, etc.). It also provides machine learning algorithms to build quality estimation models. We benchmark the framework on a number of datasets and discuss the efficacy of features and algorithms.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 The Association for Computational Linguistics. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Feb 2021 11:20 |
Last Modified: | 23 Feb 2021 11:20 |
Published Version: | https://www.aclweb.org/anthology/P13-4014 |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171412 |