A machine learning-based evaluation method for machine translation

Authors:
Katsunori Kotani;Takehiko Yoshimi
Affiliations:
Kansai Gaidai University, Osaka, Japan;Ryukoku Univeristy, Shiga, Japan
Venue:
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Year:
2010

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Abstract

Constructing a classifier that distinguishes machine translations from human translations is a promising approach to automatic evaluation of machine-translated sentences Using this approach, we constructed a classifier using Support Vector Machines based on word-alignment distributions between source sentences and human or machine translations This paper investigates the validity of the classification-based method by comparing it with well-known evaluation methods The experimental results show that our classification-based method can accurately evaluate fluency of machine translations.