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Interactive Task Learning (ITL) concerns learning about unforeseen domainconcepts via natural interactions with human users. The learner faces a numberof significant constraints: learning should be online, incremental andfew-shot, as it is expected to perform tangible belief updates right afternovel words denoting unforeseen concepts are introduced. In this work, weexplore a challenging symbol grounding task--discriminating among objectclasses that look very similar--within the constraints imposed by ITL. Wedemonstrate empirically that more data-efficient grounding results fromexploiting the truth-conditions of the teacher's generic statements (e.g., "Xshave attribute Z.") and their implicatures in context (e.g., as an answer to"How are Xs and Ys different?", one infers Y lacks attribute Z).

Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse