RDF-to-Text Generation with Graph-augmented Structural Neural Encoders
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 3030-3036. https://doi.org/10.24963/ijcai.2020/419
The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.
Machine Learning: Relational Learning
Data Mining: Mining Graphs, Semi Structured Data, Complex Data
Natural Language Processing: Natural Language Generation