@InProceedings{niehues-EtAl:2017:NMT,
  author    = {Niehues, Jan  and  Cho, Eunah  and  Ha, Thanh-Le  and  Waibel, Alex},
  title     = {Analyzing Neural MT Search and Model Performance},
  booktitle = {Proceedings of the First Workshop on Neural Machine Translation},
  month     = {August},
  year      = {2017},
  address   = {Vancouver},
  publisher = {Association for Computational Linguistics},
  pages     = {11--17},
  abstract  = {In this paper, we offer an in-depth analysis about the modeling and search
	performance. We address the question if a more complex search algorithm is
	necessary. Furthermore, we investigate the question if more complex models
	which might only be applicable during rescoring are promising.
	By separating the search space and the modeling using n-best list reranking, we
	analyze the influence of both parts of an NMT system independently. By
	comparing differently performing NMT systems, we show that the better
	translation is already in the search space of the translation systems with less
	performance. This results indicate that the current search algorithms are
	sufficient for the NMT systems. Furthermore, we could show that even a
	relatively small $n$-best list of $50$ hypotheses already contain notably
	better translations.},
  url       = {http://www.aclweb.org/anthology/W17-3202}
}