Title: 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 post-editing 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 small but
significant improvements over the baseline.