@InProceedings{ni-wang:2017:I17-2,
  author    = {Ni, Ke  and  Wang, William Yang},
  title     = {Learning to Explain Non-Standard English Words and Phrases},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {413--417},
  abstract  = {We describe a data-driven approach for automatically explaining new,
	non-standard English expressions in a given sentence, building on a large
	dataset that includes 15 years of crowdsourced examples from
	UrbanDictionary.com. Unlike prior studies that focus on matching keywords from
	a slang dictionary, we investigate the possibility of learning a neural
	sequence-to-sequence model that generates explanations of unseen non-standard
	English expressions given context. We propose a dual encoder approach---a
	word-level encoder learns the representation of context, and a second
	character-level encoder to learn the hidden representation of the target
	non-standard expression. Our model can produce reasonable definitions of new
	non-standard English expressions given their context with certain confidence.},
  url       = {http://www.aclweb.org/anthology/I17-2070}
}