Evaluating human correction quality for machine translation from crowdsourcing

Abstract

Machine translation (MT) technology is becoming more and more pervasive, yet the quality of MT output is still not ideal. Thus, human corrections are used to edit the output for further studies. However, how to judge the human correction might be tricky when the annotators are not experts. We present a novel way that uses cross-validation to automatically judge the human corrections where each MT output is corrected by more than one annotator. Cross-validation among corrections for the same machine translation, and among corrections from the same annotator are both applied. We get a correlation around 40% in sentence quality for Chinese-English and Spanish-English. We also evaluate the user quality as well. At last, we rank the quality of human corrections from good to bad, which enables us to set a quality threshold to make a trade-off between the scope and the quality of the corrections.