@InProceedings{vylomova-EtAl:2017:SCLeM,
  author    = {Vylomova, Ekaterina  and  Cohn, Trevor  and  He, Xuanli  and  Haffari, Gholamreza},
  title     = {Word Representation Models for Morphologically Rich Languages in Neural Machine Translation},
  booktitle = {Proceedings of the First Workshop on Subword and Character Level Models in NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {103--108},
  abstract  = {Out-of-vocabulary words present a great challenge for Machine Translation. 
	Recently various character-level compositional models 
	were proposed to address this issue. In current research 
	we incorporate two most popular neural architectures, namely LSTM and CNN, into
	hard- and soft-attentional models of translation for character-level
	representation of the source. We propose semantic and morphological intrinsic
	evaluation of encoder-level representations. Our analysis of the learned
	representations reveals that character-based LSTM  seems to be better at
	capturing morphological aspects compared to character-based CNN. We also show
	that hard-attentional model provides better character-level representations
	compared to vanilla one.},
  url       = {http://www.aclweb.org/anthology/W17-4115}
}