An efficient and user-friendly tool for machine translation quality estimation
Kashif Ur Rehman Shah, Marco Turchi, Lucia Specia · 2014
We present a new version of QUEST -an open source framework for machine translation quality estimation -which brings a number of improvements: (i) it provides a Web interface and functionalities such that non-expert users, e.g.translators or lay-users of machine translations, can get quality predictions (or internal features of the framework) for translations without having to install the toolkit, obtain resources or build prediction models; (ii) it significantly improves over the previous runtime performance by keeping resources (such as language models) in memory; (iii) it provides an option for users to submit the source text only and automatically obtain translations from Bing Translator; (iv) it provides a ranking of multiple translations submitted by users for each source text according to their estimated quality.We exemplify the use of this new version through some experiments with the framework.