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        <title>Syntax-based language models for statistical machine translation</title>
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    <abstract>We present a syntax-based language model for use in noisy-channel machine translation. In particular, a language model based upon that described in (Cha01) is combined with the syntax based translation-model described in (YK01). The resulting system was used to translate 347 sentences from Chinese to English and compared with the results of an IBM-model-4-based system, as well as that of (YK02), all trained on the same data. The translations were sorted into four groups: good/bad syntax crossed with good/bad meaning. While the total number of translations that preserved meaning were the same for (YK02) and the syntax-based system (and both higher than the IBM-model-4-based system), the syntax based system had 45% more translations that also had good syntax than did (YK02) (and approximately 70% more than IBM Model 4). The number of translations that did not preserve meaning, but at least had good grammar, also increased, though to less avail.</abstract>
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%0 Conference Proceedings
%T Syntax-based language models for statistical machine translation
%A Charniak, Eugene
%A Knight, Kevin
%A Yamada, Kenji
%S Proceedings of Machine Translation Summit IX: Papers
%D 2003
%8 sep 23 27
%C New Orleans, USA
%F charniak-etal-2003-syntax
%X We present a syntax-based language model for use in noisy-channel machine translation. In particular, a language model based upon that described in (Cha01) is combined with the syntax based translation-model described in (YK01). The resulting system was used to translate 347 sentences from Chinese to English and compared with the results of an IBM-model-4-based system, as well as that of (YK02), all trained on the same data. The translations were sorted into four groups: good/bad syntax crossed with good/bad meaning. While the total number of translations that preserved meaning were the same for (YK02) and the syntax-based system (and both higher than the IBM-model-4-based system), the syntax based system had 45% more translations that also had good syntax than did (YK02) (and approximately 70% more than IBM Model 4). The number of translations that did not preserve meaning, but at least had good grammar, also increased, though to less avail.
%U https://aclanthology.org/2003.mtsummit-papers.6

Markdown (Informal)

[Syntax-based language models for statistical machine translation](https://aclanthology.org/2003.mtsummit-papers.6) (Charniak et al., MTSummit 2003)

• Syntax-based language models for statistical machine translation (Charniak et al., MTSummit 2003)

ACL

• Eugene Charniak, Kevin Knight, and Kenji Yamada. 2003. Syntax-based language models for statistical machine translation. In Proceedings of Machine Translation Summit IX: Papers, New Orleans, USA.