@InProceedings{hokamp-liu:2017:Long,
  author    = {Hokamp, Chris  and  Liu, Qun},
  title     = {Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search},
  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     = {1535--1546},
  abstract  = {We present Grid Beam Search (GBS), an algorithm which extends beam search
	to allow the inclusion of pre-specified lexical constraints. The algorithm can
	be used with any model which generates sequences token by token. Lexical
	constraints take the form of phrases or words that must be present in the
	output sequence. This is a very general way to incorporate auxillary knowledge
	into a model's output without requiring any modification of the parameters or
	training data. We demonstrate the feasibility and flexibility of
	Lexically Constrained Decoding by conducting experiments on Neural
	Interactive-Predictive Translation, as well as Domain Adaptation for Neural
	Machine Translation. Experiments show that GBS can provide large improvements
	in translation quality in interactive scenarios, and that, even without any
	user input, GBS can be used to achieve significant gains in performance in
	domain adaptation scenarios.},
  url       = {http://aclweb.org/anthology/P17-1141}
}