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International Conference on Intelligent Text Processing and Computational Linguistics

CICLing 2010: Computational Linguistics and Intelligent Text Processing pp 451–463Cite as

A Maximum Entropy Approach to Syntactic Translation Rule Filtering

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6008))

Abstract

In this paper we will present a maximum entropy filter for the translation rules of a statistical machine translation system based on tree transducers. This filter can be successfully used to reduce the number of translation rules by more than 70% without negatively affecting translation quality as measured by BLEU. For some filter configurations, translation quality is even improved.

Our investigations include a discussion of the relationship of Alignment Error Rate and Consistent Translation Rule Score with translation quality in the context of Syntactic Statistical Machine Translation.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, ul. Umultowska 87, 61-614, Poznań, Poland

    Marcin Junczys-Dowmunt

Authors
  1. Marcin Junczys-Dowmunt
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Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, Mexico

    Alexander Gelbukh

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Junczys-Dowmunt, M. (2010). A Maximum Entropy Approach to Syntactic Translation Rule Filtering. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2010. Lecture Notes in Computer Science, vol 6008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12116-6_38

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  • DOI: https://doi.org/10.1007/978-3-642-12116-6_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12115-9

  • Online ISBN: 978-3-642-12116-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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