conference paper Diego Moussalem author Paramjot Kaur author Thiago Castro-Ferreira author Chris van der Lee author Conrads Felix Shimorina author Felix Conrads author René Speck author Claire Gardent author Simon Mille author Nikolai Ilinykh author Axel-Cyrille Ngonga Ngomo author The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is opensource and is publicly available along with the data it contains. Association for Computational Linguistics2020 eng 27-33 D. Moussalem, P. Kaur, T. Castro-Ferreira, C. van der Lee, C.F. Shimorina, F. Conrads, R. Speck, C. Gardent, S. Mille, N. Ilinykh, A.-C. Ngonga Ngomo, in: Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Association for Computational Linguistics, Dublin, Ireland, 2020, pp. 27–33. Moussalem, Diego, Paramjot Kaur, Thiago Castro-Ferreira, Chris van der Lee, Conrads Felix Shimorina, Felix Conrads, René Speck, et al. “A General Benchmarking Framework for Text Generation.” In <i>Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)</i>, 27–33. Dublin, Ireland: Association for Computational Linguistics, 2020. Moussalem, D., Kaur, P., Castro-Ferreira, T., van der Lee, C., Shimorina, C. F., Conrads, F., Speck, R., Gardent, C., Mille, S., Ilinykh, N., &#38; Ngonga Ngomo, A.-C. (2020). A general benchmarking framework for text generation. <i>Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)</i>, 27–33. D. Moussalem <i>et al.</i>, “A general benchmarking framework for text generation,” in <i>Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)</i>, 2020, pp. 27–33. Moussalem D, Kaur P, Castro-Ferreira T, et al. A general benchmarking framework for text generation. In: <i>Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)</i>. Association for Computational Linguistics; 2020:27-33. @inproceedings{Moussalem_Kaur_Castro-Ferreira_van der Lee_Shimorina_Conrads_Speck_Gardent_Mille_Ilinykh_et al._2020, place={Dublin, Ireland}, title={A general benchmarking framework for text generation}, booktitle={Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)}, publisher={Association for Computational Linguistics}, author={Moussalem, Diego and Kaur, Paramjot and Castro-Ferreira, Thiago and van der Lee, Chris and Shimorina, Conrads Felix and Conrads, Felix and Speck, René and Gardent, Claire and Mille, Simon and Ilinykh, Nikolai and et al.}, year={2020}, pages={27–33} } Moussalem, Diego, et al. “A General Benchmarking Framework for Text Generation.” <i>Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)</i>, Association for Computational Linguistics, 2020, pp. 27–33. 550732024-07-04T11:59:39Z2026-10-15T11:47:02Z