Big generalizations with small data: Exploring the role of training samples in learning adjectives of size

Abstract

In this paper, we experiment with a recently proposed visual reasoning task dealing with quantities - modeling the multimodal, contextually-dependent meaning of size adjectives ('big', 'small') - and explore the impact of varying the training data on the learning behavior of a state-of-art system. In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. Here, we investigate whether, and to what extent, seeing some of these cases during training helps a model understand the rule subtending the task, i.e., that being big implies being not small, and vice versa. We show that relatively few examples are enough to understand this relationship, and that developing a specific, mutually exclusive representation of size adjectives is beneficial to the task.