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Phrase-Based Statistical Machine Translation

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KI 2002: Advances in Artificial Intelligence (KI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2479))

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Abstract

This paper is based on the work carried out in the framework of the Verbmobil project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches.

In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is that it does not take contextual information into account for the translation decisions. We will present a translation model that is based on bilingual phrases to explicitly model the local context. We will show that this model performs better than the single-word based model. We will compare monotone and non-monotone search for this model and we will investigate the benefit of using the sum criterion instead of the maximum approximation.

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Zens, R., Och, F.J., Ney, H. (2002). Phrase-Based Statistical Machine Translation. In: Jarke, M., Lakemeyer, G., Koehler, J. (eds) KI 2002: Advances in Artificial Intelligence. KI 2002. Lecture Notes in Computer Science(), vol 2479. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45751-8_2

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  • DOI: https://doi.org/10.1007/3-540-45751-8_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44185-4

  • Online ISBN: 978-3-540-45751-0

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