@InProceedings{denkowski-neubig:2017:NMT,
  author    = {Denkowski, Michael  and  Neubig, Graham},
  title     = {Stronger Baselines for Trustable Results in Neural Machine Translation},
  booktitle = {Proceedings of the First Workshop on Neural Machine Translation},
  month     = {August},
  year      = {2017},
  address   = {Vancouver},
  publisher = {Association for Computational Linguistics},
  pages     = {18--27},
  abstract  = {Interest in neural machine translation has grown rapidly as its effectiveness
	has been demonstrated across language and data scenarios.  New research
	regularly introduces architectural and algorithmic improvements that lead to
	significant gains over ``vanilla'' NMT implementations.  However, these new
	techniques are rarely evaluated in the context of previously published
	techniques, specifically those that are widely used in state-of-the-art
	production and shared-task systems.  As a result, it is often difficult to
	determine whether improvements from research will carry over to systems
	deployed for real-world use.  In this work, we recommend three specific methods
	that are relatively easy to implement and result in much stronger experimental
	systems.  Beyond reporting significantly higher BLEU scores, we conduct an
	in-depth analysis of where improvements originate and what inherent weaknesses
	of basic NMT models are being addressed.  We then compare the relative gains
	afforded by several other techniques proposed in the literature when starting
	with vanilla systems versus our stronger baselines, showing that experimental
	conclusions may change depending on the baseline chosen.  This indicates that
	choosing a strong baseline is crucial for reporting reliable experimental
	results.},
  url       = {http://www.aclweb.org/anthology/W17-3203}
}