@InProceedings{su-yan:2017:EMNLP2017,
  author    = {Su, Yu  and  Yan, Xifeng},
  title     = {Cross-domain Semantic Parsing via Paraphrasing},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1235--1246},
  abstract  = {Existing studies on semantic parsing mainly focus on the in-domain setting. We
	formulate cross-domain semantic parsing as a domain adaptation problem: train a
	semantic parser on some source domains and then adapt it to the target domain.
	Due to the diversity of logical forms in different domains, this problem
	presents unique and intriguing challenges. By converting logical forms into
	canonical utterances in natural language, we reduce semantic parsing to
	paraphrasing, and develop an attentive sequence-to-sequence paraphrase model
	that is general and flexible to adapt to different domains. We discover two
	problems, small micro variance and large macro variance, of pre-trained word
	embeddings that hinder their direct use in neural networks, and propose
	standardization techniques as a remedy. On the popular Overnight dataset, which
	contains eight domains, we show that both cross-domain training and
	standardized pre-trained word embeddings can bring significant improvement.},
  url       = {https://www.aclweb.org/anthology/D17-1127}
}