Learning to Order Facts for Discourse Planning in Natural Language Generation

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This paper presents a machine learning approach to discourse planning in natural language generation. More specifically, we address the problem of learning the most natural ordering of facts in discourse plans for a specific domain. We discuss our methodology and how it was instantiated using two different machine learning algorithms. A quantitative evaluation performed in the domain of museum exhibit descriptions indicates that our approach performs significantly better than manually constructed ordering rules. Being retrainable, the resulting planners can be ported easily to other similar domains, without requiring language technology expertise.
8 pages, 4 figures, 1 table

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Computation and Language, H.5.2

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http://salesiana.dossiersoluciones.com/handle/123456789/31626

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0306062
 http://arxiv.org/abs/cs/0306062
 Proceedings of EACL 2003 Workshop on Natural Language Generation

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arXiv