@InProceedings{duong-EtAl:2017:CoNLL,
  author    = {Duong, Long  and  Afshar, Hadi  and  Estival, Dominique  and  Pink, Glen  and  Cohen, Philip  and  Johnson, Mark},
  title     = {Multilingual Semantic Parsing And Code-Switching},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {379--389},
  abstract  = {Extending semantic parsing systems to new domains and languages is a highly
	expensive, time-consuming process, so making effective use of existing
	resources is critical. In this paper, we describe a transfer learning method
	using crosslingual word embeddings in a sequence-to-sequence model.  On the
	NLmaps corpus, our approach achieves state-of-the-art accuracy of 85.7% for
	English.  Most importantly, we observed a consistent improvement for German
	compared with several baseline domain adaptation techniques.  As a by-product
	of this approach, our models that are trained on a combination of English and
	German utterances perform reasonably well on code-switching utterances which
	contain a mixture of English and German, even though the training data does not
	contain any such. As far as we know, this is the first study of code-switching
	in semantic parsing. We manually constructed the set of code-switching test 
	utterances for the NLmaps corpus and achieve 78.3% accuracy on                       
	     
	this
	dataset.},
  url       = {http://aclweb.org/anthology/K17-1038}
}