Quality Estimation for English-Hungarian Machine Translation Systems with Optimized Semantic Features

Quality estimation at run-time for machine translation systems is an important task. The standard automatic evaluation methods that use reference translations cannot evaluate MT results in real-time and the correlation between the results of these methods and that of human evaluation is very low in the case of translations from English to Hungarian. The new method to solve this problem is called quality estimation, which addresses the task by estimating the quality of translations as a prediction task for which features are extracted from the source and translated sentences only. In this study, we implement quality estimation for English-Hungarian. First, a corpus is created, which contains Hungarian human judgements. Using these human evaluation scores, different quality estimation models are described, evaluated and optimized. We created a corpus for English-Hungarian quality estimation and we developed 27 new semantic features using WordNet and word embedding models, then we created feature sets optimized for Hungarian, which produced better results than the baseline feature set.