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- W2573463306 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." @default.
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- W2573463306 date "2016-12-13" @default.
- W2573463306 modified "2023-10-16" @default.
- W2573463306 title "Building RDF Content for Data-to-Text Generation" @default.
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