Text generation: using discourse strategies and focus constraints to generate natural language text
Text generation: using discourse strategies and focus constraints to generate natural language text
Performance and evaluation of LISP systems
Performance and evaluation of LISP systems
Using focus to generate complex and simple sentences
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Description strategies for naive and expert users
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
The computational complexity of sentence derivation in functional unification grammar
COLING '86 Proceedings of the 11th coference on Computational linguistics
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
A New Level of Language Generation Technology: Capabilities and Possibilities
IEEE Expert: Intelligent Systems and Their Applications
Learning features that predict cue usage
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Types in Functional Unification Grammars
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Speech recognition in parallel
HLT '89 Proceedings of the workshop on Speech and Natural Language
Combining discourse strategies to generate descriptions to users along a naive/expert spectrum
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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In this paper, we show that one benefit of FUG, the ability to state global constraints on choice separately from syntactic rules, is difficult in generation systems based on augmented context free grammars (e.g., Definite Clause Grammars). They require that such constraints be expressed locally as part of syntactic rules and therefore, duplicated in the grammar. Finally, we discuss a reimplementation of FUG that achieves the similar levels of efficiency as Rubinoff's adaptation of MUMBLE, a deterministic language generator.