Abstract
Recently statistical methods for natural language translation have become popular and found reasonable success. In this paper we describe an English-Hindi statistical machine translation system. Our machine translation system is based on IBM Models 1, 2, and 3. We present experimental results on an English-Hindi parallel corpus consisting of 150,000 sentence pairs. We propose two new algorithms for the transfer of fertility parameters from Model 2 to Model 3. Our algorithms have a worst case time complexity of O(m 3) improving on the exponential time algorithm proposed in the classical paper on IBM Models. When the maximum fertility of a word is small, our algorithms are O(m 2) and hence very efficient in practice.
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Udupa U., R., Faruquie, T.A. (2005). An English-Hindi Statistical Machine Translation System. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_27
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DOI: https://doi.org/10.1007/978-3-540-30211-7_27
Publisher Name: Springer, Berlin, Heidelberg
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