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.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Gum-Won Hong, Seung-Wook Lee, Hae-Chang Rim
• Keywords: ACL 2009, Computational Linguistics, Function Words, Source Sentence, Unnecessary Function Words