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ACL
2009

Bridging Morpho-Syntactic Gap between Source and Target Sentences for English-Korean Statistical Machine Translation

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Bridging Morpho-Syntactic Gap between Source and Target Sentences for English-Korean Statistical Machine Translation
Often, Statistical Machine Translation (SMT) between English and Korean suffers from null alignment. Previous studies have attempted to resolve this problem by removing unnecessary function words, or by reordering source sentences. However, the removal of function words can cause a serious loss in information. In this paper, we present a possible method of bridging the morpho-syntactic gap for EnglishKorean SMT. In particular, the proposed method tries to transform a source sentence by inserting pseudo words, and by reordering the sentence in such a way that both sentences have a similar length and word order. The proposed method achieves 2.4 increase in BLEU score over baseline phrase-based system.
Gum-Won Hong, Seung-Wook Lee, Hae-Chang Rim
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Gum-Won Hong, Seung-Wook Lee, Hae-Chang Rim
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