Utilising knowledge graph embeddings for data-to-text generation

Pasricha, Nivranshu, Arcan, Mihael, & Buitelaar, Paul. (2020). Utilising knowledge graph embeddings for data-to-text generation. Paper presented at the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+) Virtual, Dublin, Ireland, 18 December.

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.

Funder

Science Foundation Ireland
European Regional Development Fund

Publisher

Association for Computational Linguistics

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

Data Science Institute (Workshop Papers)