QuEst - A translation quality estimation framework

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

Authors/Creators:	Specia, L. https://orcid.org/0000-0002-5495-3128 Shah, K. de Souza, J.G.C. Cohn, T.
Copyright, Publisher and Additional Information:	© 2013 The Association for Computational Linguistics.
Dates:	Published: August 2013
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

CORE (COnnecting REpositories)

QuEst - A translation quality estimation framework

Abstract

Metadata

Download

External copy

Export

Statistics