Conference Proceedings
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, Wei Wang, I Gurevych (ed.), Y Miyao (ed.)
PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1 | ASSOC COMPUTATIONAL LINGUISTICS-ACL | Published : 2018
DOI: 10.18653/v1/p18-1151
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..
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Awarded by Australian Research Council (ARC)
Funding Acknowledgements
Bayu Distiawan Trisedya is supported by the Indonesian Endowment Fund for Education (LPDP). This work is supported by Australian Research Council (ARC) Discovery Project DP180102050 and Future Fellowships Project FT120100832, and Google Faculty Research Award. This work is partly done while Jianzhong Qi is visiting the University of New South Wales. Wei Wang was partially supported by D2DCRC DC25002, DC25003, ARC DP 170103710 and 180103411.