@InProceedings{yu-EtAl:2017:SCLeM,
  author    = {Yu, Seunghak  and  Kulkarni, Nilesh  and  Lee, Haejun  and  Kim, Jihie},
  title     = {Syllable-level Neural Language Model for Agglutinative Language},
  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     = {92--96},
  abstract  = {We introduce a novel method to diminish the problem of out of vocabulary words
	by introducing an embedding method which leverages the agglutinative property
	of language. We propose additional embedding derived from syllables and
	morphemes for the words to improve the performance of language model. We apply
	the above method to input prediction tasks and achieve state of the art
	performance in terms of Key Stroke Saving (KSS) w.r.t. to existing device input
	prediction methods.},
  url       = {http://www.aclweb.org/anthology/W17-4113}
}