@inproceedings{84448ec9f1c64dda84d130512790b8e7,
title = "A Machine Learning Approach to Evaluating Translation Quality",
abstract = "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.",
keywords = "machine learning, Machine translation evaluation, Weka",
author = "\{Reyes Ayala\}, Brenda and Jiangping Chen",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2017 ; Conference date: 19-06-2017 Through 23-06-2017",
year = "2017",
month = jul,
day = "25",
doi = "10.1109/JCDL.2017.7991590",
language = "English (US)",
series = "Proceedings of the ACM/IEEE Joint Conference on Digital Libraries",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2017",
address = "United States",
}