Semantic Triples Verbalization with Generative Pre-Training Model

Pavel Blinov


Abstract
The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters’ influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.
Anthology ID:
2020.webnlg-1.17
Volume:
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Month:
12
Year:
2020
Address:
Dublin, Ireland (Virtual)
Venue:
WebNLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
154–158
Language:
URL:
https://aclanthology.org/2020.webnlg-1.17
DOI:
Bibkey:
Cite (ACL):
Pavel Blinov. 2020. Semantic Triples Verbalization with Generative Pre-Training Model. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 154–158, Dublin, Ireland (Virtual). Association for Computational Linguistics.
Cite (Informal):
Semantic Triples Verbalization with Generative Pre-Training Model (Blinov, WebNLG 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2020.webnlg-1.17.pdf
Code
 blinovpd/ru-rdf2text