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

By Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang. 2018

91 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Natural Language Generation

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Citations in the corpus

DART: Open-Domain Structured Data Record to Text Generation (2020, background): “, 2019), Resource Description Framework (RDF triples) (Gardent et al., 2017b; Vougiouklis et al., 2018; Distiawan et al., 2018; Elsahar et al., 2018; Zhu et al., 2019b), logic forms (Chen et al.”
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation (2020, methodology): “More recent methods use Seq2Seq architecture (Sutskever et al., 2014) to combine planning and realization into an end-to-end network and have achieved the state-of-the-art on a variety of generation tasks (Lebret et al., 2016; Trisedya et al., 2018; Juraska et al., 2018; Reed et al., 2018).”
ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020, background): “, 2018; Perez-Beltrachini and Lapata, 2018), descriptions of knowledge bases (ODonnell et al., 2000; Trisedya et al., 2018; Zhu et al., 2019; Yu et al., 2019) and source code (Iyer et al.”
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT (2023, unknown): “A.5 Text Graph Year Method Task Code 2016 Li et al. [165] Text-to-KG Generation - 2019 KG-BERT [166] Text-to-KG Generation https://github.com/yao8839836/kg-bert 2020 Malaviya et al. [167] Text-to-KG Generation https://github.com/allenai/commonsense-kg-completion 2019 Petroni et al. citepetroni2019la”
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers (2020, unknown): “Trisedya et al. (2018) introduced a GTR-LSTM triple encoder.”
Entity Alignment between Knowledge Graphs Using Attribute Embeddings (2019, methodology): “Knowledge bases in the form of knowledge graphs (KGs) have been used in many applications including question answering systems (Yih et al. 2015), recommender systems (Zhang et al. 2016), and sentence generation for a Geographic Knowledge Base (Trisedya et al. 2018; GKB 2017).”
PaperRobot: Incremental Draft Generation of Scientific Ideas (2019, background): “…knowledge bases (Duma and Klein, 2013; Konstas and Lap-ata, 2013; Flanigan et al., 2016; Hardy and Vla-chos, 2018; Pourdamghani et al., 2016; Trisedya et al., 2018; Xu et al., 2018; Madotto et al., 2018; Nie et al., 2018), biographies based on attributes (Lebret et al., 2016; Chisholm et…”
Structural Information Preserving for Graph-to-Text Generation (2020, background): “Previous work for neural graph-to-text generation (Konstas et al., 2017; Song et al., 2018; Beck et al., 2018; Trisedya et al., 2018; Marcheggiani and Perez-Beltrachini, 2018; Xu et al., 2018; Cao and Clark, 2019; Damonte and Cohen, 2019; Ha-jdik et al., 2019; Koncel-Kedziorski et al., 2019; Hong…”
Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task (2020, background): “To address this problem, triple-speciﬁc or graph-based encoders have been proposed to better process the KG triple input (Vougiouklis et al., 2018; Distiawan et al., 2018; Marcheggiani and Perez-Beltrachini, 2018; Schmitt et al., 2020; Zhao et al., 2020).”
Investigating Pretrained Language Models for Graph-to-Text Generation (2020, methodology): “Prior approaches for KG-to-text generation train text-to-text neural models using sequences of KG triples as input (Trisedya et al., 2018; Moryossef et al., 2019; Castro Ferreira et al., 2019; Ribeiro et al., 2021a).”
Handling Rare Items in Data-to-Text Generation (2018, background): “To address these issues, neural approaches to data-to-text generation typically resort either to delexi-calisation (Wen et al., 2015; Duˇsek and Jurcicek, 2015; Trisedya et al., 2018; Chen et al., 2018) or to a copy mechanism (Chen, 2018; Elder et al., 2018; Gehrmann et al., 2018).”