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Transductive learning for statistical machine translation

Nicola Ueffing and Gholamreza Haffari and Anoop Sarkar


The paper is about the use of transductive semi-supervised methods for the effective use of monolingual data from the source language in order to improve translation quality. Transductive means that they repeatedly translate sentences from the development set or test set and use generated translation to improve the SMT system. Transductive learning is another mean to adapt the SMT system to a new type of text.

Authors mention two SMT modeling problems which need different learning strategies for improving the translation quality.

1. SMT systems face data sparseness issue even if there is large bitext available for any language pair.
2. For many language pairs the amount of available bilingual text is very limited.

The authors hypothesis is that adding information from source data might help in improvements.


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