@InProceedings{shimorina-gardent:2018:W18-65,
  author    = {Shimorina, Anastasia  and  Gardent, Claire},
  title     = {Handling Rare Items in Data-to-Text Generation},
  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     = {360--370},
  abstract  = {Neural approaches to data-to-text generation generally handle rare iput items using either delexicalisation or the copy mechanism. We investigate the relative impact of these two approaches on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that while copy and coverage markedly improved results compared to a setting where delexicalisation is not applied, delexicalisation usually performs better than copy and coverage; (ii) that in the more challenging evaluation setting where the number of rare items is greater, the performances of copying decreases; and (iii) that the impact of these two mechanisms varies greatly depending on how the dataset is constructed and on how it is split into dev, test and train.},
  url       = {http://www.aclweb.org/anthology/W18-6543}
}