Describing a Knowledge Base
Abstract
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new \emph{table position self-attention} to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% - 72.6% F-score.
Cite
@article{arxiv.1809.01797,
title = {Describing a Knowledge Base},
author = {Qingyun Wang and Xiaoman Pan and Lifu Huang and Boliang Zhang and Zhiying Jiang and Heng Ji and Kevin Knight},
journal= {arXiv preprint arXiv:1809.01797},
year = {2020}
}
Comments
12 pages. Accepted by The 11th International Conference on Natural Language Generation (INLG 2018) Code at https://github.com/EagleW/Describing_a_Knowledge_Base