  The language we use over the course of conversation changes as we establish
common ground and learn what our partner finds meaningful. Here we draw upon
recent advances in natural language processing to provide a finer-grained
characterization of the dynamics of this learning process. We release an open
corpus (>15,000 utterances) of extended dyadic interactions in a classic
repeated reference game task where pairs of participants had to coordinate on
how to refer to initially difficult-to-describe tangram stimuli. We find that
different pairs discover a wide variety of idiosyncratic but efficient and
stable solutions to the problem of reference. Furthermore, these conventions
are shaped by the communicative context: words that are more discriminative in
the initial context (i.e. that are used for one target more than others) are
more likely to persist through the final repetition. Finally, we find
systematic structure in how a speaker's referring expressions become more
efficient over time: syntactic units drop out in clusters following positive
feedback from the listener, eventually leaving short labels containing
open-class parts of speech. These findings provide a higher resolution look at
the quantitative dynamics of ad hoc convention formation and support further
development of computational models of learning in communication.
