Abstract
Standard stochastic grammars use generative probabilistic models, focusing on rewriting probabilities conditioned by the symbol to be rewritten. Among several other undesired behaviors, such grammars tend to give penalty to longer derivations of the same input, which is a drawback when they are used for analysis (rather than generation). In this contribution, we propose a novel non-generative probabilistic model for both Stochastic Context-Free Grammars (SCFGs) and Stochastic Tree-Substitution Grammars (STSGs), in which the probabilities are conditioned by the leaves (i.e. the input symbols) rather than by the root of the parse tree. Both theoretical and experimental improvements of these new models are presented.
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Rozenknop, A., Chappelier, JC., Rajman, M. (2004). Discriminative Models of SCFG and STSG. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_24
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DOI: https://doi.org/10.1007/978-3-540-30120-2_24
Publisher Name: Springer, Berlin, Heidelberg
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