Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text in the target language. In this paper we explore the use of transductive semi-supervised methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French-English EuroParl data set and on data from the NIST Chinese-English largedata track. We show a significant improvement in translation quality on both tasks.
REF further refined this approach by using transductive semi-supervised methods for effective use of monolingual data from the source text.
Transductive learning for statistical machine translation
45th Annual Meeting of the Association of Computational Linguistics
Computer Science