Utilising Knowledge Graph Embeddings for Data-to-Text Generation

Nivranshu Pasricha, Mihael Arcan, Paul Buitelaar


Abstract
Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their utility for end-to-end approaches in a data-to-text generation task. Our experiments show that using knowledge graph embeddings can yield an improvement of up to 2 – 3 BLEU points for seen categories on the WebNLG corpus without modifying the underlying neural network architecture.
Anthology ID:
2020.webnlg-1.6
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:
48–53
Language:
URL:
https://aclanthology.org/2020.webnlg-1.6
DOI:
Bibkey:
Cite (ACL):
Nivranshu Pasricha, Mihael Arcan, and Paul Buitelaar. 2020. Utilising Knowledge Graph Embeddings for Data-to-Text Generation. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 48–53, Dublin, Ireland (Virtual). Association for Computational Linguistics.
Cite (Informal):
Utilising Knowledge Graph Embeddings for Data-to-Text Generation (Pasricha et al., WebNLG 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.webnlg-1.6.pdf
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