Exploring Linguistic Probes for Morphological Generalization

SIGMORPHON and SIGMORPHON-UniMorph Three languages were chosen whose inflectional shared tasks (Cotterell et al., 2016, 2017, 2018; morphologies range from entirely fusional (English), McCarthy et al., 2019; Vylomova et al., 2020; Pimentel to mixed (Spanish), to mostly agglutinative et al., 2021; Kodner et al., 2022) as well (Swahili). In highly agglutinative languages, individual as in more targeted studies focused on specific languages features in a set tend to correspond to distinct or the generalization behavior of computational morphological patterns, so a model may generalize models (Goldman et al., 2022; Wiemerslage to unseen feature sets by mapping component et al., 2022; Kodner et al., 2023b; Guriel et al., features to their corresponding patterns. This is 2023; Kodner et al., 2023a), is to train on (lemma, exemplified by the Swahili example (1), in which inflection, features) triples and predict inflected most features correspond to individual morphemes; forms from held-out (lemma, features) only the person/number prefix maps to more than pairs.