QuEst: A framework for translation quality estimation

Lucia Specia, Kashif Shah


Abstract
We present QUEST, an open source framework for translation quality estimation. QUEST provides a wide range of feature extractors from source and translation texts and external resources and tools. These go from simple, language-independent features, to advanced, linguistically motivated features. They include features that rely on information from the translation system and features that are oblivious to the way translations were produced. In addition, it provides wrappers for a well-known machine learning toolkit, scikit-learn, including techniques for feature selection and model building, as well as parameter optimisation. We also present a Web interface and functionalities for non-expert users. Using this interface, quality predictions (or internal features of the framework) can be obtained without the installation of the toolkit and the building of prediction models. The interface also provides a ranking method for multiple translations given for the same source text according to their predicted quality.
Anthology ID:
2014.amta-wptp.12
Volume:
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas
Month:
October 22-26
Year:
2014
Address:
Vancouver, Canada
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
121
Language:
URL:
https://aclanthology.org/2014.amta-wptp.12
DOI:
Bibkey:
Cite (ACL):
Lucia Specia and Kashif Shah. 2014. QuEst: A framework for translation quality estimation. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas, page 121, Vancouver, Canada. Association for Machine Translation in the Americas.
Cite (Informal):
QuEst: A framework for translation quality estimation (Specia & Shah, AMTA 2014)
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PDF:
https://aclanthology.org/2014.amta-wptp.12.pdf