Specia, L. orcid.org/0000-0002-5495-3128, Paetzold, G.H. and Scarton, C. orcid.org/0000-0002-0103-4072 (2015) Multi-level translation quality prediction with QuEst++. In: Proceedings of ACL-IJCNLP 2015 System Demonstrations. The 53rd Annual Meeting of the Association for Computational Linguistics and The 7th International Joint Conference on Natural Language Processing, 26 Jul 2015 - 31 Jul 2016, Beijing, China. Association for Computational Linguistics (ACL) , pp. 115-120. ISBN 9781941643990
Abstract
This paper presents QUEST++ , an open source tool for quality estimation which can predict quality for texts at word, sentence and document level. It also provides pipelined processing, whereby predictions made at a lower level (e.g. for words) can be used as input to build models for predictions at a higher level (e.g. sentences). QUEST++ allows the extraction of a variety of features, and provides machine learning algorithms to build and test quality estimation models. Results on recent datasets show that QUEST++ achieves state-of-the-art performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 The Association for Computational Linguistics and The Asian Federation of Natural Language Processing. |
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 11:26 |
Last Modified: | 23 Feb 2021 11:26 |
Published Version: | https://www.aclweb.org/anthology/P15-4020 |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Identification Number: | 10.3115/v1/P15-4020 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171413 |