@InProceedings{kato-EtAl:2017:W17-55,
  author    = {Kato, Tsuneo  and  Nagai, Atsushi  and  Noda, Naoki  and  Sumitomo, Ryosuke  and  Wu, Jianming  and  Yamamoto, Seiichi},
  title     = {Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {60--64},
  abstract  = {Recursive autoencoders (RAEs) for compositionality of a vector space model were
	applied to utterance intent classification of a smartphone-based
	Japanese-language spoken dialogue system. Though the RAEs express a nonlinear
	operation on the vectors of child nodes, the operation is considered to be
	different intrinsically depending on types of child nodes. To relax the
	difference, a data-driven untying of autoencoders (AEs) is proposed. The
	experimental result of the utterance intent classification showed an improved
	accuracy with the proposed method compared with the basic tied RAE and untied
	RAE based on a manual rule.},
  url       = {http://aclweb.org/anthology/W17-5508}
}