Abstract
This paper describes the Resource Description Framework (RDF) triples verbalizer developed for the WEB NLG CHALLENGE 2020 shared task. After reviewing representative works in Natural Language Generation in the context of the Semantic Web, the task is then described. We then sketch the symbolic approach we used for verbalizing RDF triples: once the triples are grouped by subject, each group is realized as one or more sentences using templates written in Python whose output is feed to an English realizer written in Javascript. The system was developed using the test data of the previous edition of the task and the train and development data of this year’s task. The automatic scores for this year’s test data are quite competitive. We conclude with a critical review of the data and discuss the suitability of this competition results in a wider Natural Language Generation setting.- Anthology ID:
- 2020.webnlg-1.16
- Volume:
- Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
- Month:
- 12
- Year:
- 2020
- Address:
- Dublin, Ireland (Virtual)
- Editors:
- Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
- Venue:
- WebNLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 144–153
- Language:
- URL:
- https://aclanthology.org/2020.webnlg-1.16
- DOI:
- Cite (ACL):
- Guy Lapalme. 2020. RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 144–153, Dublin, Ireland (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples (Lapalme, WebNLG 2020)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2020.webnlg-1.16.pdf