Transductive learning for statistical machine translation

Nicola Ueffing, Gholamreza Haffari, Anoop Sarkar

Abstract: 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.