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.

In this paper, we frame the problem of co-reference resolution in dialogue as a dynamic social process in which mentions to people previously known and newly introduced are mixed when people know each other well. We restructured an existing data set for the Friends sitcom as a coreference task that evolves over time, where close friends make reference to other people either part of their common ground (inner circle) or not (outer circle). We expect that awareness of common ground is key in social dialogue in order to resolve references to the inner social circle, whereas local contextual information plays a more important role for outer circle mentions. Our analysis of these references confirms that there are differences in naming and introducing these people. We also experimented with the SpanBERT coreference system with and without fine-tuning to measure whether preceding discourse contexts matter for resolving inner and outer circle mentions. Our results show that more inner circle mentions lead to a decrease in model performance, and that fine-tuning on preceding contexts reduces false negatives for both inner and outer circle mentions but increases the false positives as well, showing that the models overfit on these contexts.

We present EMISSOR: a platform to capture multimodal interactions as recordings of episodic experiences with explicit referential interpretations that also yield an episodic Knowledge Graph (eKG). The platform stores streams of multiple modalities as parallel signals. Each signal is segmented and annotated independently with interpretation. Annotations are eventually mapped to explicit identities and relations in the eKG. As we ground signal segments from different modalities to the same instance representations, we also ground different modalities across each other. Unique to our eKG is that it accepts different interpretations across modalities, sources and experiences and supports reasoning over conflicting information and uncertainties that may result from multimodal experiences. EMISSOR can record and annotate experiments in virtual and real-world, combine data, evaluate system behavior and their performance for preset goals but also model the accumulation of knowledge and interpretations in the Knowledge Graph as a result of these episodic experiences.