learning algorithms

A short response rejecting the scientific contribution of language models as theories.

A novel method for sampling a class of subsequential string transductions encoding homomorphisms allows rigorous testing of learning models’ capacity for compositionality.

This chapter examines the brief but vibrant history of learnability in phonology.

A book chapter on mathematical theories of language and learning, and their consequences for linguistic cognition studies.

We derive the well-studied subregular classes of formal languages, which computationally characterize natural language typology, purely from the perspective of algorithmic learning problems.

We comment on non-human animals’ ability to learn syntactic vs phonological dependencies in pattern-learning experiments.

We overview vowel harmony computationally, describing necessary and sufficient conditions on phonotactics, processes, and learning.

We comment on mathematical fallacies present in artificial grammar learning experiments and suggest how to integrate psycholinguistic and mathematical results.

We analyze the expressivity of a variety of recurrent encoder-decoder networks, showing they are limited to learning subsequential functions, and connecting RNNs with attention mechanisms to a class of deterministic 2-way transducers.

I provide a vector space characterization of the Star-Free and Locally Threshold testable classes of formal languages, over arbitrary data structures.