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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 – 8 of 8 1 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ... Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya. - : arXiv, 2021 BASE Show details 2 One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech ... Nekvinda, Tomáš; Dušek, Ondřej. - : arXiv, 2020 BASE Show details 3 Neural Generation for Czech: Data and Baselines ... Dušek, Ondřej; Jurčíček, Filip. - : arXiv, 2019 BASE Show details 4 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 5 RankME: Reliable Human Ratings for Natural Language Generation ... Novikova, Jekaterina; Dušek, Ondřej; Rieser, Verena. - : arXiv, 2018 Abstract: Human evaluation for natural language generation (NLG) often suffers from inconsistent user ratings. While previous research tends to attribute this problem to individual user preferences, we show that the quality of human judgements can also be improved by experimental design. We present a novel rank-based magnitude estimation method (RankME), which combines the use of continuous scales and relative assessments. We show that RankME significantly improves the reliability and consistency of human ratings compared to traditional evaluation methods. In addition, we show that it is possible to evaluate NLG systems according to multiple, distinct criteria, which is important for error analysis. Finally, we demonstrate that RankME, in combination with Bayesian estimation of system quality, is a cost-effective alternative for ranking multiple NLG systems. ... : Accepted to NAACL 2018 (The 2018 Conference of the North American Chapter of the Association for Computational Linguistics) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1803.05928 https://arxiv.org/abs/1803.05928 BASE Hide details 6 Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity ... Xu, Xinnuo; Dušek, Ondřej; Konstas, Ioannis. - : arXiv, 2018 BASE Show details 7 Findings of the E2E NLG Challenge ... Dušek, Ondřej; Novikova, Jekaterina; Rieser, Verena. - : arXiv, 2018 BASE Show details 8 The E2E Dataset: New Challenges For End-to-End Generation ... Novikova, Jekaterina; Dušek, Ondřej; Rieser, Verena. - : arXiv, 2017 BASE Show details

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