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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge ... Dušek, Ondřej; Novikova, Jekaterina; Rieser, Verena. - : arXiv, 2019 BASE Show details 2 RankME: Reliable Human Ratings for Natural Language Generation ... Novikova, Jekaterina; Dušek, Ondřej; Rieser, Verena. - : arXiv, 2018 BASE Show details 3 Findings of the E2E NLG Challenge ... Dušek, Ondřej; Novikova, Jekaterina; Rieser, Verena. - : arXiv, 2018 BASE Show details 4 The E2E Dataset: New Challenges For End-to-End Generation ... Novikova, Jekaterina; Dušek, Ondřej; Rieser, Verena. - : arXiv, 2017 Abstract: This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data. ... : Accepted as a short paper for SIGDIAL 2017 (final submission including supplementary material) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7 URL: https://arxiv.org/abs/1706.09254 https://dx.doi.org/10.48550/arxiv.1706.09254 BASE Hide details

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