@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}