@InProceedings{masumura-EtAl:2018:SIGdial,
  author    = {Masumura, Ryo  and  Tanaka, Tomohiro  and  Ando, Atsushi  and  Ishii, Ryo  and  Higashinaka, Ryuichiro  and  Aono, Yushi},
  title     = {Neural Dialogue Context Online End-of-Turn Detection},
  booktitle = {Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {224--228},
  abstract  = {This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker's utterances and collocutor's utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker's and collocutor's multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker's acoustic sequential features and collocutor's linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker's utterances and collocutor's utterances into consideration.},
  url       = {http://www.aclweb.org/anthology/W18-5024}
}