Transferring Learned Models of Morphological Analogy

Abstract

Analogical proportions are statements of the form “A is to B as C is to D”, which have been extensively studied in morphology. Recent advances on learning models of analogy from quadruples pave the way for data-driven modeling and analysis of analogy. In morphology, recent work introduces a neural network classifier for morphological analogies (ANNc). In this paper, we study the transferability of ANNc across different axiomatic settings to show the importance of the data augmentation in the modeling of analogy. We also provide experimental results on transfer between two morphology datasets (Sigmorphon2016 and Sigmorphon2019) and between more than 27 languages to draw parallels between transfer performance and proximity between language families.