GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data

Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, Wei Wang


Abstract
A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6%, 6.0%, and 16.4% in three common metrics BLEU, METEOR, and TER, respectively.
Anthology ID:
P18-1151
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1627–1637
Language:
URL:
https://aclanthology.org/P18-1151
DOI:
10.18653/v1/P18-1151
Bibkey:
Cite (ACL):
Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, and Wei Wang. 2018. GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1627–1637, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data (Trisedya et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-1151.pdf
Poster:
 P18-1151.Poster.pdf
Data
WebNLG