Abstract
We present a new technique to estimate the reliability of the words in automatically generated translations. Our approach addresses confidence estimation as a classification problem where a confidence score is to be predicted from a feature vector that represents each translated word. We describe a new set of prediction features designed to capture context information, and propose a model based on partial least squares to perform the classification. Good empirical results are reported in a large-domain news translation task.
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González-Rubio, J., Navarro-Cerdán, J.R., Casacuberta, F. (2013). Partial Least Squares for Word Confidence Estimation in Machine Translation. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_59
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DOI: https://doi.org/10.1007/978-3-642-38628-2_59
Publisher Name: Springer, Berlin, Heidelberg
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