SimpleNets: Machine Translation Quality Estimation with Resource-Light Neural Networks

Abstract

We introduce SimpleNets: a resource-light solution to the sentence-level Quality Estimation task of WMT16 that combines Recurrent Neural Networks, word embedding models, and the principle of compositionality. The SimpleNets systems explore the idea that the quality of a translation can be derived from the quality of its n-grams. This approach has been successfully employed in Text Simplification quality assessment in the past. Our experiments show that, surprisingly, our models can learn more about a translation's quality by focusing on the original sentence, rather than on the translation itself.