The WebNLG Challenge: Generating Text from RDF Data

Claire Gardent, Anastasia Shimorina, Shashi Narayan, Laura Perez-Beltrachini


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.
Anthology ID:
W17-3518
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
124–133
Language:
URL:
https://aclanthology.org/W17-3518
DOI:
10.18653/v1/W17-3518
Bibkey:
Cite (ACL):
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017. The WebNLG Challenge: Generating Text from RDF Data. In Proceedings of the 10th International Conference on Natural Language Generation, pages 124–133, Santiago de Compostela, Spain. Association for Computational Linguistics.
Cite (Informal):
The WebNLG Challenge: Generating Text from RDF Data (Gardent et al., INLG 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-3518.pdf
Data
WebNLG