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.
Type de document :
Communication dans un congrès
The 26th International Conference on Computational Linguistics (COLING 2016), Dec 2016, Osaka, Japan
https://hal.inria.fr/hal-01623800
Contributeur : Claire Gardent
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Soumis le : mercredi 25 octobre 2017 - 16:35:00
Dernière modification le : mardi 24 avril 2018 - 13:30:47
Document(s) archivé(s) le : vendredi 26 janvier 2018 - 14:55:33
Laura Perez-Beltrachini, Rania Sayed, Claire Gardent. Building RDF Content for Data-to-Text Generation. The 26th International Conference on Computational Linguistics (COLING 2016), Dec 2016, Osaka, Japan. 〈hal-01623800〉
