@InProceedings{rama-ccoltekin:2016:VarDial3,
  author    = {Rama, Taraka  and  \c{C}\"{o}ltekin, \c{C}a\u{g}rı},
  title     = {LSTM Autoencoders for Dialect Analysis},
  booktitle = {Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {25--32},
  abstract  = {Computational approaches for dialectometry employed Levenshtein distance to
	compute an aggregate similarity between two dialects belonging to a single
	language group. In this paper, we apply a sequence-to-sequence autoencoder to
	learn a deep representation for words that can be used for meaningful
	comparison across dialects. In contrast to the alignment-based methods, our
	method does not require explicit alignments. We apply our architectures to
	three different datasets and show that the learned representations indicate
	highly similar results with the analyses based on Levenshtein distance and
	capture the traditional dialectal differences shown by dialectologists.},
  url       = {http://aclweb.org/anthology/W16-4803}
}