Okapi+QuEst: Translation Quality Estimation within Okapi

摘要

Due to the ever growing applicability of machine translation, estimating the quality of translations automatically has become a necessary task in various scenarios, for example, when deciding whether a machine translation is good enough for human post-editing. This demonstration presents the outcome of a collaborative project between the University of Sheffield and ENLASO, funded by EAMT, the European Association for Machine Translation. The project aimed to integrate a lightweight and user-friendly version of QuEst - a quality estimation toolkit, into Okapi- a framework with various components and applications designed to help create and improve translation and localisation processes. As result, Okapi users are now offered a software plugin to build and apply quality estimation models for translations produced within the framework. In addition to the standard functionalities of QuEst, the project involved the creation of new methods to facilitate the generation of the linguistic resources necessary for building quality estimation models.