SIGMORPHON–UniMorph 2022 Shared Task 0: Modeling Inflection in Language Acquisition
Jordan Kodner, Salam Khalifa · 2022
This year's iteration of the SIGMORPHON-UniMorph shared task on "human-like" morphological inflection generation focuses on generalization and errors in language acquisition.Systems are trained on data sets extracted from corpora of child-directed speech in order to simulate a natural learning setting, and their predictions are evaluated against what is known about children's developmental trajectories for three well-studied patterns: English past tense, German noun plurals, and Arabic noun plurals.Three submitted neural systems were evaluated together with two baselines.Performance was generally good, and all systems were prone to human-like over-regularization.However, all systems were also prone to non-human-like over-irregularization and nonsense productions to varying degrees.We situate this behavior in a discussion of the Past Tense Debate. 1