Abstract
When automatically translating between related languages, one of the main sources of machine translation errors is the incorrect resolution of part-of-speech (PoS) ambiguities. Hidden Markov models (HMM) are the standard statistical approach to try to properly resolve such ambiguities. The usual training algorithms collect statistics from source-language texts in order to adjust the parameters of the HMM, but if the HMM is to be embedded in a machine translation system, target-language information may also prove valuable. We study how to use a target-language model (in addition to source-language texts) to improve the tagging and translation performance of a statistical PoS tagger of an otherwise rule-based, shallow-transfer machine translation engine, although other architectures may be considered as well. The method may also be used to customize the machine translation engine to a particular target language, text type, or subject, or to statistically “retune” it after introducing new transfer rules.
Work funded by the Spanish Government through grants TIC2003-08681-C02-01 and BES-2004-4711. We thank Rafael C. Carrasco for useful comments on this work. We also thank Geoffrey Sampson (University of Sussex, England) for his Simple Good-Turing implementation.
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Sánchez-Martínez, F., Pérez-Ortiz, J.A., Forcada, M.L. (2004). Exploring the Use of Target-Language Information to Train the Part-of-Speech Tagger of Machine Translation Systems. In: Vicedo, J.L., Martínez-Barco, P., Muńoz, R., Saiz Noeda, M. (eds) Advances in Natural Language Processing. EsTAL 2004. Lecture Notes in Computer Science(), vol 3230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30228-5_13
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DOI: https://doi.org/10.1007/978-3-540-30228-5_13
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