@InProceedings{stahlberg-EtAl:2017:EMNLP2017Demos,
  author    = {Stahlberg, Felix  and  Hasler, Eva  and  Saunders, Danielle  and  Byrne, Bill},
  title     = {SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {25--30},
  abstract  = {This paper introduces SGNMT, our experimental platform for machine translation
	research. SGNMT provides a generic interface to neural and symbolic scoring
	modules (predictors) with left-to-right semantic such as translation models
	like NMT, language models, translation lattices, n-best lists or other kinds of
	scores and constraints. Predictors can be combined with other predictors to
	form complex decoding tasks. SGNMT implements a number of search strategies for
	traversing the space spanned by the predictors which are appropriate for
	different predictor constellations. Adding new predictors or decoding
	strategies is particularly easy, making it a very efficient tool for
	prototyping new research ideas. SGNMT is actively being used by students in the
	MPhil program in Machine Learning, Speech and Language Technology at the
	University of Cambridge for course work and theses, as well as for most of the
	research work in our group.},
  url       = {http://www.aclweb.org/anthology/D17-2005}
}