Interactive Acquisition of Fine-grained Visual Concepts by Exploiting
  Semantics of Generic Characterizations in Discourse
Jonghyuk Park Alex Lascarides Subramanian Ramamoorthy
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).