@InProceedings{gervits-eberhard-scheutz:2016:COLING,
  author    = {Gervits, Felix  and  Eberhard, Kathleen  and  Scheutz, Matthias},
  title     = {Disfluent but effective? A quantitative study of disfluencies and conversational moves in team discourse},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3359--3369},
  abstract  = {Situated dialogue systems that interact with humans as part of a team (e.g.,
	robot teammates) need to be able to use information from communication channels
	to gauge the coordination level and effectiveness of the team. Currently, the
	feasibility of this end goal is limited by several gaps in both the empirical
	and computational literature. The purpose of this paper is to address those
	gaps in the following ways: (1) investigate which properties of task-oriented
	discourse correspond with effective performance in human teams, and (2) discuss
	how and to what extent these properties can be utilized in spoken dialogue
	systems. To this end, we analyzed natural language data from a unique corpus of
	spontaneous, task-oriented dialogue (CReST corpus), which was annotated for
	disfluencies and conversational moves. We found that effective teams made more
	self-repair disfluencies and used specific communication strategies to
	facilitate
	  grounding and coordination. Our results indicate that truly robust and
	natural dialogue systems will need to interpret highly disfluent utterances and
	also utilize specific collaborative mechanisms to facilitate grounding. These
	data shed light on effective communication in performance scenarios and
	directly inform the development of robust dialogue systems for situated
	artificial agents.},
  url       = {http://aclweb.org/anthology/C16-1317}
}