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The WebNLG Challenge: Generating Text from RDF Data

Abstract : The WebNLG challenge consists in mapping sets of RDF triples to text. It provides a common benchmark on which to train, evaluate and compare “microplanners”, i.e. generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregation, lexicalisation, surface realisation and sentence segmentation. In this paper, we introduce the microplanning task, describe data preparation, introduce our evaluation methodology, analyse participant results and provide a brief description of the participating systems.
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https://hal.archives-ouvertes.fr/hal-02461197
Contributor : Anastasia Shimorina Connect in order to contact the contributor
Submitted on : Thursday, January 30, 2020 - 3:03:28 PM
Last modification on : Wednesday, November 24, 2021 - 9:54:14 AM

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Claire Gardent, Anastasia Shimorina, Shashi Narayan, Laura Perez-Beltrachini. The WebNLG Challenge: Generating Text from RDF Data. Proceedings of the 10th International Conference on Natural Language Generation, Sep 2017, Santiago de Compostela, Spain. pp.124-133, ⟨10.18653/v1/W17-3518⟩. ⟨hal-02461197⟩

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