@inproceedings{pasricha-etal-2020-utilising,
title = "Utilising Knowledge Graph Embeddings for Data-to-Text Generation",
author = "Pasricha, Nivranshu and
Arcan, Mihael and
Buitelaar, Paul",
editor = "Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia",
booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
month = "12",
year = "2020",
address = "Dublin, Ireland (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.webnlg-1.6",
pages = "48--53",
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.",
}
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%0 Conference Proceedings
%T Utilising Knowledge Graph Embeddings for Data-to-Text Generation
%A Pasricha, Nivranshu
%A Arcan, Mihael
%A Buitelaar, Paul
%Y Castro Ferreira, Thiago
%Y Gardent, Claire
%Y Ilinykh, Nikolai
%Y van der Lee, Chris
%Y Mille, Simon
%Y Moussallem, Diego
%Y Shimorina, Anastasia
%S Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland (Virtual)
%F pasricha-etal-2020-utilising
%X 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.
%U https://aclanthology.org/2020.webnlg-1.6
%P 48-53
Markdown (Informal)
[Utilising Knowledge Graph Embeddings for Data-to-Text Generation](https://aclanthology.org/2020.webnlg-1.6) (Pasricha et al., WebNLG 2020)
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.