What transfers in morphological inflection? Experiments with analogical models

We investigate 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. We show that this model is capable of near-baseline performance on the SigMorphon 2020 inflection challenge. Such a model can make predictions for unseen languages, allowing us to perform one-shot inflection on natural languages and investigate 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. We argue that the degree of generality of these process representations also helps to explain transfer results from previous research.