An efficient and user-friendly tool for machine translation quality estimation

摘要

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.