Keith Holyoak, Department of Psychology, UCLA
Friday, October 23, 2020
Abstract Semantic Relations in Mind, Brain, and Machines
Abstract semantic relations (e.g., category membership, part-whole, antonymy, cause-effect) are central to human intelligence, underlying the distinctively human ability to reason by analogy. I will describe a computational project (Bayesian Analogy with Relational Transformations) that aims to extract explicit representations of abstract semantic relations from non-relational inputs automatically generated by machine learning. BART’s representations predict patterns of typicality and similarity for semantic relations, as well as similarity of neural signals triggered by semantic relations during analogical reasoning. In this approach, analogy emerges from the ability to learn and compare relations; mapping emerges later from the ability to compare patterns of relations.
Richard Futrell, Department of Language Science, UC Irvine
Friday, September 18, 2020
Efficiency-based models of natural language: Predicting word order universals Using information theory
Why is human language the way it is? I claim that human languages can be modeled as systems for efficient communication: that is, codes that maximize information transfer subject to constraints on the cognitive resources used during language production and comprehension. I use this efficiency-based framework to formulate quantitative theories of word order, aiming to explain the cross-linguistic universals of word order documented by linguists as well as the statistical distribution of word orders in massively cross-linguistic corpus studies.
I present three results. First, I show that word orders in 54 languages are shaped by dependency locality: a pressure for words linked in syntactic dependencies to be close to each other, which minimizes working memory usage during language processing. Second, I introduce a new model of information-processing difficulty in online language processing, which simultaneously captures effects of probabilistic expectations and working memory constraints, recovering dependency locality as a special case and making new predictions, in particular about adjective order. Third, I present a computational framework in which grammars can be directly optimized for efficiency. When grammars are optimized to maximize information transfer while minimizing processing difficulty, they end up reproducing 8 typological universals of word order.