@inproceedings{trisedya-etal-2018-gtr,
title = "{GTR}-{LSTM}: A Triple Encoder for Sentence Generation from {RDF} Data",
author = "Trisedya, Bayu Distiawan and
Qi, Jianzhong and
Zhang, Rui and
Wang, Wei",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1151",
doi = "10.18653/v1/P18-1151",
pages = "1627--1637",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
%A Trisedya, Bayu Distiawan
%A Qi, Jianzhong
%A Zhang, Rui
%A Wang, Wei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F trisedya-etal-2018-gtr
%X 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.
%R 10.18653/v1/P18-1151
%U https://aclanthology.org/P18-1151
%U https://doi.org/10.18653/v1/P18-1151
%P 1627-1637
Markdown (Informal)
[GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data](https://aclanthology.org/P18-1151) (Trisedya et al., ACL 2018)
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.