@InProceedings{wang-EtAl:2018:W18-65,
  author    = {Wang, Qingyun  and  Pan, Xiaoman  and  Huang, Lifu  and  Zhang, Boliang  and  Jiang, Zhiying  and  Ji, Heng  and  Knight, Kevin},
  title     = {Describing a Knowledge Base},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {10--21},
  abstract  = {We aim to automatically generate natural language narratives 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 table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we also 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.},
  url       = {http://www.aclweb.org/anthology/W18-6502}
}