@inproceedings{katsakioris-etal-2019-corpus,
    title = "Corpus of Multimodal Interaction for Collaborative Planning",
    author = "Katsakioris, Miltiadis Marios  and
      Hastie, Helen  and
      Konstas, Ioannis  and
      Laskov, Atanas",
    editor = "Bhatia, Archna  and
      Bisk, Yonatan  and
      Kordjamshidi, Parisa  and
      Thomason, Jesse",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-1601",
    doi = "10.18653/v1/W19-1601",
    pages = "1--6",
    abstract = "As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals. To this end, we conducted a Wizard of Oz study to gather data and investigate the way operators would collaboratively make plans via a conversational {`}planning assistant{'} for remote autonomous systems. We present here a corpus of 22 dialogs from expert operators, which can be used to train such a system. Data analysis shows that multimodality is key to successful interaction, measured both quantitatively and qualitatively via user feedback.",
}