Publication
Sentence-level quality estimation by predicting HTER as a multi-component metric
Eleftherios Avramidis
In: Second Conference on Machine Translation. Workshop on Statistical Machine Translation (WMT-17), located at The 2017 Conference on Empirical Methods in Natural Language Processing, September 7-8, Copenhagen, Denmark, Association for Computational Linguistics, 9/2017.
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.