This paper presents QUEST++ , an open source tool for quality estimation which can predict quality for texts at word, sentence and document level. It also provides pipelined processing, whereby predictions made at a lower level (e.g. for words) can be used as input to build models for predictions at a higher level (e.g. sentences). QUEST++ allows the extraction of a variety of features, and provides machine learning algorithms to build and test quality estimation models. Results on recent datasets show that QUEST++ achieves state-of-the-art performance.
A recent work on word-level QE is QuEst++ REF , which is an open-source toolkit for QE on word, sentence and document level.
Multi-level Translation Quality Prediction with QuEst++
ACL
Computer Science