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• W1515680992 abstract "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." @default.
• W1515680992 created "2016-06-24" @default.
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• W1515680992 date "2003-01-01" @default.
• W1515680992 modified "2023-09-24" @default.
• W1515680992 title "Learning to Order Facts for Discourse Planning in Natural Language Generation." @default.
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