Quantity, Contrast, and Convention in Cross-Situated Language Comprehension

Abstract

Typically, visually-grounded language learning systems only accept feature data about objects in the environment that are explicitly mentioned, whether through annotation labels or direct reference through natural language. We show that when objects are described ambiguously using natural language, a system can use a combination of the pragmatic principles of Contrast and Conventionality, and multiple-instance learning to learn from ambiguous examples in an online fashion. Applying child language learning strategies to visual learning enables more effective learning in real-time environments, which can lead to enhanced teaching interactions with robots or grounded systems in multi-object environments.