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In visual instruction-following dialogue games, players can engage in repairmechanisms in face of an ambiguous or underspecified instruction that cannot befully mapped to actions in the world. In this work, we annotate InstructionClarification Requests (iCRs) in CoDraw, an existing dataset of interactions ina multimodal collaborative dialogue game. We show that it contains lexicallyand semantically diverse iCRs being produced self-motivatedly by playersdeciding to clarify in order to solve the task successfully. With 8.8k iCRsfound in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus,making it a valuable resource for data-driven research on clarification indialogue. We then formalise and provide baseline models for two tasks:Determining when to make an iCR and how to recognise them, in order toinvestigate to what extent these tasks are learnable from data.

Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset