@InProceedings{wang-EtAl:2017:I17-13,
  author    = {Wang, Xin  and  Wang, Jianan  and  Liu, Yuanchao  and  Wang, Xiaolong  and  Wang, Zhuoran  and  Wang, Baoxun},
  title     = {Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues},
  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     = {713--722},
  abstract  = {User experience is essential for human-computer dialogue systems. However, it
	is impractical to ask users to provide explicit feedbacks when the agents'
	responses displease them. Therefore, in this paper, we explore to predict
	users' imminent dissatisfactions caused by intelligent agents by analysing the
	existing utterances in the dialogue sessions. To our knowledge, this is the
	first work focusing on this task. Several possible factors that trigger
	negative emotions are modelled. A relation sequence model (RSM) is proposed to
	encode the sequence of appropriateness of current response with respect to the
	earlier utterances. The experimental results show that the proposed structure
	is effective in modelling emotional risk (possibility of negative feedback)
	than existing conversation modelling approaches. Besides, strategies of
	obtaining distance supervision data for pre-training are also discussed in this
	work. Balanced sampling with respect to the last response in the distance
	supervision data are shown to be reliable for data augmentation.},
  url       = {http://www.aclweb.org/anthology/I17-1072}
}