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        <title>Improved Phrase-based SMT with Syntactic Reordering Patterns Learned from Lattice Scoring</title>
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        <namePart type="given">Jie</namePart>
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    <abstract>In this paper, we present a novel approach to incorporate source-side syntactic reordering patterns into phrase-based SMT. The main contribution of this work is to use the lattice scoring approach to exploit and utilize reordering information that is favoured by the baseline PBSMT system. By referring to the parse trees of the training corpus, we represent the observed reorderings with source-side syntactic patterns. The extracted patterns are then used to convert the parsed inputs into word lattices, which contain both the original source sentences and their potential reorderings. Weights of the word lattices are estimated from the observations of the syntactic reordering patterns in the training corpus. Finally, the PBSMT system is tuned and tested on the generated word lattices to show the benefits of adding potential source-side reorderings in the inputs. We confirmed the effectiveness of our proposed method on a medium-sized corpus for Chinese-English machine translation task. Our method outperformed the baseline system by 1.67% relative on a randomly selected testset and 8.56% relative on the NIST 2008 testset in terms of BLEU score.</abstract>
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%0 Conference Proceedings
%T Improved Phrase-based SMT with Syntactic Reordering Patterns Learned from Lattice Scoring
%A Jiang, Jie
%A Du, Jinhua
%A Way, Andy
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F jiang-etal-2010-improved
%X In this paper, we present a novel approach to incorporate source-side syntactic reordering patterns into phrase-based SMT. The main contribution of this work is to use the lattice scoring approach to exploit and utilize reordering information that is favoured by the baseline PBSMT system. By referring to the parse trees of the training corpus, we represent the observed reorderings with source-side syntactic patterns. The extracted patterns are then used to convert the parsed inputs into word lattices, which contain both the original source sentences and their potential reorderings. Weights of the word lattices are estimated from the observations of the syntactic reordering patterns in the training corpus. Finally, the PBSMT system is tuned and tested on the generated word lattices to show the benefits of adding potential source-side reorderings in the inputs. We confirmed the effectiveness of our proposed method on a medium-sized corpus for Chinese-English machine translation task. Our method outperformed the baseline system by 1.67% relative on a randomly selected testset and 8.56% relative on the NIST 2008 testset in terms of BLEU score.
%U https://aclanthology.org/2010.amta-papers.11

Markdown (Informal)

[Improved Phrase-based SMT with Syntactic Reordering Patterns Learned from Lattice Scoring](https://aclanthology.org/2010.amta-papers.11) (Jiang et al., AMTA 2010)

• Improved Phrase-based SMT with Syntactic Reordering Patterns Learned from Lattice Scoring (Jiang et al., AMTA 2010)

ACL

• Jie Jiang, Jinhua Du, and Andy Way. 2010. Improved Phrase-based SMT with Syntactic Reordering Patterns Learned from Lattice Scoring. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.