Data enhancement and selection strategies for the word-level Quality Estimation

This paper describes the DCU-SHEFF word-level Quality Estimation (QE) system submitted to the QE shared task at WMT15. Starting from a baseline set of features and a CRF algorithm to learn a sequence tagging model, we propose improvements in two ways: (i) by filtering out the training sentences containing too few errors, and (ii) by adding incomplete… CONTINUE READING