A Recurrent Neural Networks Approach for Estimating the Quality of Machine Translation Output

Abstract

This paper presents a novel approach using recurrent neural networks for estimating the quality of machine translation output. A sequence of vectors made by the prediction method is used as the input of the final recurrent neural network. The prediction method uses bi-directional recurrent neural network architecture both on source and target sentence to fully utilize the bi-directional quality information from source and target sentence. Our experiments show that the proposed recurrent neural networks approach achieves a performance comparable to the existing state-of-the-art models for estimating the sentence-level quality of English-to-Spanish translation.