Were We There Already? A pplying Minimal Generalization to the SIGMORPHON - U ni M orph Shared Task on Cognitively Plausible Morphological Inflection (2021)

Abstract: Morphological rules with various levels of specificity can be learned from example lexemes by recursive application of minimal generalization (Albright and Hayes, 2002, 2003). A model that learns rules solely through minimal generalization was used to predict average human wug-test ratings from German, English, and Dutch in the SIGMORPHON-UniMorph 2021 Shared Task, with competitive results. Some formal properties of the minimal generalization operation were proved. An automatic method was developed to create wug-test stimuli for future experiments that investigate whether the model’s morphological generalizations are too minimal.

## introduction in a landmark paper, @xcite proposed a model that learns morphological rules by recursive minimal generalization from lexemespecific examples (e.g., @xmath0, +coronal, +anterior, . . . ] n). 1 the model was presented more formally in @xcite , along with evidence that the rules it learns for the english past tense give a good account of native speakers' productions and ratings in wug-test experiments (e.g., judgments that splung is quite acceptable as the past tense of the novel verb spling). in addition to providing further analysis of the behavioral data, @xcite compared their proposal with early connectionist models of morphology (e.g., @xcite and an analogical or 'family resemblance' model inspired by research on psychological categories @xcite . along with @xcite , which presents a parallel treatment of italian inflection, albright & hayes's study of the english past tense is an ideal example of theory-driven, multiple-methodology, open and reproducible research in cognitive science. 2 their model has enduring significance for the study of morphological learning and productivity in english (e.g., @xcite @xcite and many other languages (e.g., hijazi arabic: ahyad 2019; japanese: oseki et al. 2019; korean: @xcite @xcite ; tgdaya seediq: kuo 2020; spanish: @xcite . in this study, we applied a partial reimplementation of the @xcite model to wug-test rating data from three languages (german, english, and dutch) collected for the sigmorphon-unimorph 2021 shared task. our version of the model is based purely on minimal generalization of morphological rules, as described in §3.1 of @xcite and reviewed below. it does not include additional mechanisms for learning phonological rules, and further expanding or reigning in morphological rules, that were part of the original model (see @xcite .3 - §3.7). we think it is 2 albright & hayes released both the results of their wug-test experiments and an implementation of their model (see http://www.mit.edu/~albright/mgl/ and https://linguistics.ucla.edu/people/ hayes/rulesvsanalogy/index.html ). an impediment to large-scale simulation with the model is that it runs from a gui interface only. as part of the present project, we have added a command line interface to the original source code and converted the english input files to a more user-friendly format (available on request). we are aware of one other implementation of the minimal generalization model, due to joão veríssimo, but this was unavailable at the time of our study ( https://www.jverissimo.net/resources ). ## system description and results our system for the shared task preprocesed the input wordforms, learned rules with recursive minimal generalization, scored the rules in two alternative ways, pruned rule that have no effect on the model's predictions, and applied the remaining rules to wug forms to yield predicted ratings. ## summary and future directions we have described the minimal generalization operation for morphological rules as proposed by albright & hayes and presented some new formal results on this operation. we have also described our partial implementation of their model -a pure minimal generalization learner -and applied it to wug-test data from three related languages. we conclude with some remarks on how our implementation could be extended and how the central concept of minimal generalization could be empirically tested in future behavioral experiments.