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.
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⟩
