HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download

Contributor : Claire Gardent Connect in order to contact the contributor
Submitted on : Wednesday, October 25, 2017 - 4:35:00 PM
Last modification on : Wednesday, November 24, 2021 - 9:54:10 AM
Long-term archiving on: : Friday, January 26, 2018 - 2:55:33 PM


Publisher files allowed on an open archive


  • HAL Id : hal-01623800, version 1



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⟩



Record views


Files downloads