Abstract
Phrase-based and hierarchical phrase-based (Hiero) translation models differ radically in the way reordering is modeled. Lexicalized reordering models play an important role in phrase-based MT and such models have been added to CKY-based decoders for Hiero. Watanabe et al. (2006) proposed a promising decoding algorithm for Hiero (LR-Hiero) that visits input spans in arbitrary order and produces the translation in left to right (LR) order which leads to far fewer language model calls and leads to a considerable speedup in decoding. We introduce a novel shift-reduce algorithm to LR-Hiero to decode with our lexicalized reordering model (LRM) and show that it improves translation quality for Czech-English, Chinese-English and German-English.
Highlights
Phrase-based machine translation handles reordering between source and target languages by visiting phrases in the source in arbitrary order while generating the target from left to right
We show that augmenting left to right (LR)-Hierarchical phrase-based translation (Hiero) with an lexicalized reordering model (LRM) improves translation quality for Czech-English, significantly improves results for Chinese-English and German-English, while performing three times fewer language model queries on average, compared to CKY-Hiero
We have proposed a novel lexicalized reordering model (LRM) for the left-to-right variant of Hiero called LR-Hiero distinct from previous LRM models
Summary
Phrase-based machine translation handles reordering between source and target languages by visiting phrases in the source in arbitrary order while generating the target from left to right. State-of-the-art phrase based translation systems address this issue by applying a lexicalized reordering model (LRM) (Tillmann, 2004; Koehn et al, 2007; Galley and Manning, 2008; Galley and Manning, 2010) which uses word aligned data to score phrase pair reordering. Nguyen and Vogel (2013) integrate phrase-based distortion and lexicalized reordering features with CKY-based Hiero decoder which significantly improve the translation quality. They use a LRM trained for phrase-based MT (Galley and Manning, 2010) which applies some restrictions on the Hiero rules. We show that augmenting LR-Hiero with an LRM improves translation quality for Czech-English, significantly improves results for Chinese-English and German-English, while performing three times fewer language model queries on average, compared to CKY-Hiero
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