Rania Sayed


2016

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Building RDF Content for Data-to-Text Generation
Laura Perez-Beltrachini | Rania Sayed | Claire Gardent
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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.