@inproceedings{kruijt-etal-2024-spotter,
    title = "{SPOTTER}: A Framework for Investigating Convention Formation in a Visually Grounded Human-Robot Reference Task",
    author = "Kruijt, Jaap  and
      van Minkelen, Peggy  and
      Donatelli, Lucia  and
      Vossen, Piek T.J.M.  and
      Konijn, Elly  and
      Baier, Thomas",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1322",
    pages = "15202--15215",
    abstract = "Linguistic conventions that arise in dialogue reflect common ground and can increase communicative efficiency. Social robots that can understand these conventions and the process by which they arise have the potential to become efficient communication partners. Nevertheless, it is unclear how robots can engage in convention formation when presented with both familiar and new information. We introduce an adaptable game platform, SPOTTER, to study the dynamics of convention formation for visually grounded referring expressions in both human-human and human-robot interaction. Specifically, we seek to elicit convention forming for members of an inner circle of well-known individuals in the common ground, as opposed to individuals from an outer circle, who are unfamiliar. We release an initial corpus of 5000 utterances from two exploratory pilot experiments in Dutch. Different from previous work focussing on human-human interaction, we find that referring expressions for both familiar and unfamiliar individuals maintain their length throughout human-robot interaction. Stable conventions are formed, although these conventions can be impacted by distracting outer circle individuals. With our distinction between familiar and unfamiliar, we create a contrastive operationalization of common ground, which aids research into convention formation.",
}

Linguistic conventions that arise in dialogue reflect common ground and can increase communicative efficiency. Social robots that can understand these conventions and the process by which they arise have the potential to become efficient communication partners. Nevertheless, it is unclear how robots can engage in convention formation when presented with both familiar and new information. We introduce an adaptable game platform, SPOTTER, to study the dynamics of convention formation for visually grounded referring expressions in both human-human and human-robot interaction. Specifically, we seek to elicit convention forming for members of an inner circle of well-known individuals in the common ground, as opposed to individuals from an outer circle, who are unfamiliar. We release an initial corpus of 5000 utterances from two exploratory pilot experiments in Dutch. Different from previous work focussing on human-human interaction, we find that referring expressions for both familiar and unfamiliar individuals maintain their length throughout human-robot interaction. Stable conventions are formed, although these conventions can be impacted by distracting outer circle individuals. With our distinction between familiar and unfamiliar, we create a contrastive operationalization of common ground, which aids research into convention formation.
Kruijt, Jaap van Minkelen, Peggy Donatelli, Lucia Vossen, Piek T.J.M. Konijn, Elly Baier, Thomas
SPOTTER: A Framework for Investigating Convention Formation in a Visually Grounded Human-Robot Reference Task