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- W2902624514 abstract "Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets." @default.
- W2902624514 created "2018-12-11" @default.
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- W2902624514 date "2018-01-01" @default.
- W2902624514 modified "2023-09-23" @default.
- W2902624514 title "Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning" @default.
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- W2902624514 doi "https://doi.org/10.18653/v1/w18-6546" @default.
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