- Abstract:
- The process of microplanning in natural language generation (NLG) encompasses a range of problems in which a generator must bridge underlying domain-specific representations and general linguistic representations. These problems include constructing linguistic referring expressions to identify domain objects, selecting lexical items to express domain concepts, and using complex linguistic constructions to concisely convey related domain facts. In this paper, we argue that such problems are best solved through a uniform, comprehensive, declarative process. In our approach, the generator directly explores a search space for utterances described by a linguistic grammar. At each stage of search, the generator uses a model of interpretation, which characterizes the potential links between the utterance and the domain and context, to assess its progress in conveying domain-specific representations. We further address the challenges for implementation and knowledge representation in this approach. We show how to implement this approach effectively by using the lexicalized tree-adjoining grammar (LTAG) formalism to connect structure to meaning and using modal logic programming to connect meaning to context. We articulate a detailed methodology for designing grammatical and conceptual resources which the generator can use to achieve desired microplanning behavior in a specified domain.
- Links To Paper
- 1st Link
- Bibtex format
- @Article{EDI-INF-RR-1084,
- author = {
Matthew Stone
and Christine Doran
and Tonya Bleam
and Martha Palmer
and Bonnie Webber
},
- title = {Micro-Planning based on Communicative Intent: The SPUD System},
- journal = {Computational Intelligence},
- publisher = {Blackwell},
- year = 2003,
- month = {Nov},
- volume = {19},
- pages = {311-381},
- doi = {10.1046/j.0824-7935.2003.00221.x},
- url = {http://www.blackwell-synergy.com/links/doi/10.1046%2Fj.0824-7935.2003.00221.x},
- }
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