Experiments on domain adaptation for English-Hindi SMT
Haque, Rejwanul and Naskar, Sudip Kumar and van Genabith, Josef and Way, Andy (2009) Experiments on domain adaptation for English-Hindi SMT. In: PACLIC 23 - the 23rd Pacific Asia Conference on Language, Information and Computation, 3-5 December 2009, Hong Kong.
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Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for English—Hindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora
with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline.
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