Improving machine translation quality estimation with neural network features

Abstract

Machine translation quality estimation is a challenging task in the WMT evaluation campaign. Feature extraction plays an important role in automatic quality estimation, and in this paper, we propose neural network features, including embedding features and cross-entropy features of source sentences and machine translations, to improve machine translation quality estimation. The sentence embedding features are extracted through global average pooling from word embedding and are trained by the word2vec toolkits, while the sentence cross-entropy features are calculated by the recurrent neural network language model. The experimental results on the development set of WMT17 machine translation quality estimation tasks show that the neural network features gain significant improvements over the baseline. Furthermore, when combining the neural network features and the baseline features, the system performance obtains further improvement.