Speaker: Roni Katzir (MIT and Tel Aviv University) Title: Choosing between theories of UG using compression-based learning Time: Thursday March 9, 12:30-2:30pm Room: 32-D461 Abstract:
I will discuss an approach to learning — compression-based learning — and show how it can help us choose between competing grammatical architectures in some cases where adult judgments alone are insufficiently informative.
Compression (or the principle of Minimum Description Length; also very closely related to Bayesian approaches) considers both the size of the grammar and that of the description of the data given the grammar and attempts to minimize their sum. By doing so, compression guides the learner to hypotheses that balance between generality and the need to fit the data. Compression appears to match subjects’ generalization patterns in a variety of tasks, and it has yielded working learners for realistic linguistic theories in different domains.
I will review these properties of compression-based learning and show how we can use it to compare between competing architectures with two case studies, one in phonology and one in semantics. The phonological case study concerns constraints on underlying representations (also known as morpheme-structure constraints), which were central to early generative phonology but rejected in Optimality Theory. Evidence bearing directly on the question of whether the grammar uses constraints on URs has been scarce. I will show, however, that if the child is a compression-based learner, then they will succeed in learning patterns such as English aspiration if they can use constraints on URs but run into difficulties otherwise. In semantics, I will discuss two architectures for the representation of quantificational determiners: building blocks and semantic automata. While both choices support the representation and learning of quantificational determiners, I will show a specific domain where they predict different learning paths.