@InProceedings{huang-EtAl:2018:N18-22,
  author    = {Huang, Po-Sen  and  Wang, Chenglong  and  Singh, Rishabh  and  Yih, Wen-tau  and  He, Xiaodong},
  title     = {Natural Language to Structured Query Generation via Meta-Learning},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {732--738},
  abstract  = {In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%--5.4% absolute accuracy gains over the non-meta-learning counterparts.},
  url       = {http://www.aclweb.org/anthology/N18-2115}
}