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.
@article{arxiv.1307.1872,
title = {Intelligent Hybrid Man-Machine Translation Quality Estimation},
author = {Ibrahim Sabek and Noha A. Yousri and Nagwa Elmakky and Mona Habib},
journal= {arXiv preprint arXiv:1307.1872},
year = {2013}
}