What transfers in morphological inflection? Experiments with analogical models
Micha Elsner · 2021
This paper investigates how abstract processes like suffixation can be learned from morphological inflection task data using an analogical memory-based framework.In this framework, the inflection target form is specified by providing an example inflection of another word in the language.This model is capable of near-baseline performance on the Sig-Morphon 2020 inflection challenge.Such a model can make predictions for unseen languages, allowing one-shot inflection for natural languages and the investigation of morphological transfer with synthetic probes.Accuracy for one-shot transfer can be unexpectedly high for some target languages (88% in Shona) and language families (53% across Romance).Probe experiments show that the model learns partially generalizable representations of prefixation, suffixation and reduplication, aiding its ability to transfer.The paper argues that the degree of generality of these process representations also helps to explain transfer results from previous research.