Learning the Meaning of Quantifiers from Language and Vision

Pezzelle, Sandro (2018) Learning the Meaning of Quantifiers from Language and Vision. PhD thesis, University of Trento.

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Defining the meaning of vague quantifiers (‘few’, ‘most’, ‘all’) has been, and still is, the Holy Grail of a mare magnum of studies in philosophy, logic, and linguistics. The way by which they are learned by children has been largely investigated in the realm of language acquisition, and the mechanisms underlying their comprehension and processing have received attention from experimental pragmatics, cognitive psychology, and neuroscience. Very often their meaning has been tied to that of numbers, amounts, and proportions, and many attempts have been made to place them on ordered scales. In this thesis, I study quantifiers from a novel, cognitively-inspired computational perspective. By carrying out several behavioral studies with human speakers, I seek to answer several questions concerning their meaning and use: Is the choice of quantifiers modulated by the linguistic context? Do quantifiers lie on a mental, semantically-ordered scale? Which are the features of such a scale? By exploiting recent advances in computational linguistics and computer vision, I test the performance of state-of-art neural networks in performing the same tasks and propose novel architectures to model speakers’ use of quantifiers in grounded contexts. In particular, I ask the following questions: Can the meaning of quantifiers be learned from visual scenes? How does this mechanism compare with that subtending comparatives, numbers, and proportions? The contribution of this work is two-fold: On the cognitive level, it sheds new light on various issues concerning the meaning and use of such expressions, and provides experimental evidence supporting the validity of the foundational theories. On the computational level, it proposes a novel, theoretically-informed approach to the modeling of vague and context-dependent expressions from both linguistic and visual data. By carefully analyzing the performance and errors of the models, I show the effectiveness of neural networks in performing challenging, high-level tasks. At the same time, I highlight commonalities and differences with human behavior.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Cognitive and Brain Sciences
PhD Cycle:31
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Area 10 - Scienze dell'antichità, filologico-letterarie e storico-artistiche > L-LIN/01 GLOTTOLOGIA E LINGUISTICA
Repository Staff approval on:07 Dec 2018 09:23

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