@InProceedings{zhao-kawahara:2017:I17-1,
  author    = {Zhao, Tianyu  and  Kawahara, Tatsuya},
  title     = {Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {704--712},
  abstract  = {Dialog act segmentation and recognition are basic natural language
	understanding tasks in spoken dialog systems. This paper investigates a unified
	architecture for these two tasks, which aims to improve the model's performance
	on both of the tasks. Compared with past joint models, the proposed
	architecture can (1) incorporate contextual information in dialog act
	recognition, and (2) integrate models for tasks of different levels as a whole,
	i.e. dialog act segmentation on the word level and dialog act recognition on
	the segment level. Experimental results show that the joint training system
	outperforms the simple cascading system and the joint coding system on both
	dialog act segmentation and recognition tasks.},
  url       = {http://www.aclweb.org/anthology/I17-1071}
}