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)
- 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:
- 48–53
- Language:
- URL:
- https://aclanthology.org/2020.webnlg-1.6
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.webnlg-1.6.pdf
- Data
- DBpedia