利用话语中通用描述的语义交互式获取细粒度视觉概念
Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse
AI总结：
本研究在交互式任务学习的在线、增量、小样本约束下，探索细粒度视觉概念的符号接地任务，通过利用教师通用陈述的真值条件与语境隐含义，提升了接地的数据效率。
摘要：
交互式任务学习（ITL）关注通过与人类用户的自然交互来学习未预见的领域概念。学习者面临诸多显著约束：学习需是在线、增量且小样本的，因为在表示未预见概念的新词被引入后，它需要立即执行切实的信念更新。本研究在ITL施加的约束下，探索了一项具有挑战性的符号接地任务——区分外观高度相似的物体类别。我们通过实验证明，利用教师通用陈述（如“X具有属性Z”）的真值条件及其在语境中的隐含义（例如，作为“X和Y有何不同？”的回答，可推断Y不具备属性Z），能实现数据效率更高的接地。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
