Building RDF Content for Data-to-Text Generation. COLING 2016.

Date : September 2016

Rania Mohammed, Laura Perez-Beltrachini and Claire Gardent. Building RDF Content for Data-to-Text Generation. COLING 2016, The 26th International Conference on Computational Linguistics., Osaka, Japan. December 11-16, 2016.

Abstract : In Natural Language Generation (NLG), one important limitation is thelack 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.

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