Proceedings of the Second Conference on Machine Translation 2017
DOI: 10.18653/v1/w17-4758
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Sentence-level quality estimation by predicting HTER as a multi-component metric

Abstract: This submission investigates alternative machine learning models for predicting the HTER score on the sentence level. Instead of directly predicting the HTER score, we suggest a model that jointly predicts the amount of the 4 distinct postediting operations, which are then used to calculate the HTER score. This also gives the possibility to correct invalid (e.g. negative) predicted values prior to the calculation of the HTER score. Without any feature exploration, a multi-layer perceptron with 4 outputs yields… Show more

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