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Building RDF Content for Data-to-Text Generation
Abstract : In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation. Using both automated metrics and a human evaluation, we show that the data units produced by our method are both diverse and coherent.
Cited literature [8 references]
https://hal.inria.fr/hal-01623800
Contributor : Claire Gardent <>
Submitted on : Wednesday, October 25, 2017 - 4:35:00 PM
Last modification on : Tuesday, December 18, 2018 - 4:38:02 PM
Document(s) archivé(s) le : Friday, January 26, 2018 - 2:55:33 PM
Contributor : Claire Gardent <>
Submitted on : Wednesday, October 25, 2017 - 4:35:00 PM
Last modification on : Tuesday, December 18, 2018 - 4:38:02 PM
Document(s) archivé(s) le : Friday, January 26, 2018 - 2:55:33 PM
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final-coling2016-rania-cs.pdf
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