A machine learning approach to evaluating translation quality
Brenda Reyes Ayala, Jiangping Chen · ACM/IEEE Joint Conference on Digital Libraries · 2017
We explored supervised machine learning (ML) techniques to understand and predict the adequacy and fluency of English-Spanish machine translation. Five experiments were conducted using three classifiers in Weka, an open-source ML tool. We found that the highest performance was achieved by applying a dimensionality reduction approach to the classification task, which included collapsing a numeric scale of quality to two categories: high quality and low quality. Our results showed that the Support Vector Machine classifier performed the best at predicting the adequacy (65.65%) and fluency (65.77%) of the translations. More research is needed to explore the methodologies of applying ML to translation evaluation.