  Inferring evaluation scores based on human judgments is invaluable compared
to using current evaluation metrics which are not suitable for real-time
applications e.g. post-editing. However, these judgments are much more
expensive to collect especially from expert translators, compared to evaluation
based on indicators contrasting source and translation texts. This work
introduces a novel approach for quality estimation by combining learnt
confidence scores from a probabilistic inference model based on human
judgments, with selective linguistic features-based scores, where the proposed
inference model infers the credibility of given human ranks to solve the
scarcity and inconsistency issues of human judgments. Experimental results,
using challenging language-pairs, demonstrate improvement in correlation with
human judgments over traditional evaluation metrics.
