Characterizing morphological productivity in neural language models

This work examines artificial neural language model behavior through the lens of morphological productivity. Specifically, we ask: given a corpus of linguistic data, (a) can we predict what morphological generalizations a transformer language model trained on this corpus will learn, and (b) is this learned behavior predicted by any existing theories of morphological productivity in humans? To…