Transductive learning for statistical machine translation
Nicola Ueffing, Gholamreza Haffari, Anoop Sarkar · NPARC · 2007
Statistical machine translation systems are usually trained on large amounts of bilin-gual text and monolingual text in the tar-get language. In this paper we explore the use of transductive semi-supervised meth-ods for the effective use of monolingual data from the source language in order to im-prove translation quality. We propose sev-eral 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 large-data track. We show a significant improve-ment in translation quality on both tasks. 1