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We motivate and describe a new freely available human-human dialogue datasetfor interactive learning of visually grounded word meanings through ostensivedefinition by a tutor to a learner. The data has been collected using a novel,character-by-character variant of the DiET chat tool (Healey et al., 2003;Mills and Healey, submitted) with a novel task, where a Learner needs to learninvented visual attribute words (such as " burchak " for square) from a tutor.As such, the text-based interactions closely resemble face-to-face conversationand thus contain many of the linguistic phenomena encountered in natural,spontaneous dialogue. These include self-and other-correction, mid-sentencecontinuations, interruptions, overlaps, fillers, and hedges. We also present ageneric n-gram framework for building user (i.e. tutor) simulations from thistype of incremental data, which is freely available to researchers. We showthat the simulations produce outputs that are similar to the original data(e.g. 78% turn match similarity). Finally, we train and evaluate aReinforcement Learning dialogue control agent for learning visually groundedword meanings, trained from the BURCHAK corpus. The learned policy showscomparable performance to a rule-based system built previously.

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings