  Interactive Task Learning (ITL) concerns learning about unforeseen domain
concepts via natural interactions with human users. The learner faces a number
of significant constraints: learning should be online, incremental and
few-shot, as it is expected to perform tangible belief updates right after
novel words denoting unforeseen concepts are introduced. In this work, we
explore a challenging symbol grounding task--discriminating among object
classes that look very similar--within the constraints imposed by ITL. We
demonstrate empirically that more data-efficient grounding results from
exploiting the truth-conditions of the teacher's generic statements (e.g., "Xs
have 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).
