@InProceedings{skadina-pinnis:2017:I17-1,
  author    = {Skadina, Inguna  and  Pinnis, M\={a}rcis},
  title     = {NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {373--383},
  abstract  = {The recent technological shift in machine translation from statistical machine
	translation (SMT) to neural machine translation (NMT) raises the question of
	the strengths and weaknesses of NMT. In this paper, we present an analysis of
	NMT and SMT systems' outputs from narrow domain English-Latvian MT systems that
	were trained on a rather small amount of data. We analyze post-edits produced
	by professional translators and manually annotated errors in these outputs.
	Analysis of post-edits allowed us to conclude that both approaches are
	comparably successful, allowing for an increase in translators' productivity,
	with the NMT system showing slightly worse results. Through the analysis of
	annotated errors, we found that NMT translations are more fluent than SMT
	translations. However, errors related to accuracy, especially, mistranslation
	and omission errors, occur more often in NMT outputs. The word form errors,
	that characterize the morphological richness of Latvian, are frequent for both
	systems, but slightly fewer in NMT outputs.},
  url       = {http://www.aclweb.org/anthology/I17-1038}
}