23 March 2017, Computational Linguistics Seminar, Raffaella Bernardi
Linguistics quantifiers have been the realm of Formal Semantics. A lot is known about their formal properties and how those properties affect logical entailment, the licensing of polarity item, or scope ambiguities. Less is known about how quantifiers are acquired by children and even less about how computational models can learn to quantify objects in images. In this talk, we will report on our findings in this direction. First of all, we will explain why the task is interesting and challenging for a Language and Vision model. Secondly, we will report our evaluation of state-of-the-art neural network models against this task. Thirdly, we will compare the acquisition of quantifiers with the acquisition of cardinals. We will show that a model capitalizing on a `fuzzy' measure of similarity is effective for learning quantifiers, whereas the learning of exact cardinals is better accomplished when information about number is provided.