@InProceedings{tong-EtAl:2017:Long,
  author    = {Tong, Edmund  and  Zadeh, Amir  and  Jones, Cara  and  Morency, Louis-Philippe},
  title     = {Combating Human Trafficking with Multimodal Deep Models},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1547--1556},
  abstract  = {Human trafficking is a global epidemic affecting millions of people across the
	planet. Sex trafficking, the dominant form of human trafficking, has seen a
	significant rise mostly due to the abundance of escort websites, where human
	traffickers can openly advertise among at-will escort advertisements. In this
	paper, we take a major step in the automatic detection of advertisements
	suspected to pertain to human trafficking. We present a novel dataset called
	Trafficking-10k, with more than 10,000~advertisements annotated for this task.
	The dataset contains two sources of information per advertisement: text and
	images. For the accurate detection of trafficking advertisements, we designed
	and trained a deep multimodal model called the Human Trafficking Deep Network
	(HTDN).},
  url       = {http://aclweb.org/anthology/P17-1142}
}