Abstract
Stochastic Inversion Transduction Grammars are a very powerful formalism in Machine Translation that allow to parse a string pair with efficient Dynamic Programming algorithms. The usual parsing algorithms that have been previously defined cannot explore the complete search space. In this work, we propose important modifications that consider the whole search space. We formally prove the correctness of the new algorithm. Experimental work shows important improvements in the probabilistic estimation of the models when using the new algorithm.
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Gascó, G., Sánchez, JA., Benedí, JM. (2010). Complete Search Space Exploration for SITG Inside Probability. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_29
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DOI: https://doi.org/10.1007/978-3-642-14980-1_29
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