Abstract
Neural network models of morphological inflection (NNMIs) have seen impressive improvements over the last several decades. These improvements, however, lie in raw accuracy and other practical issues. When the performance of these models is instead compared to that of child learners, many of the shortcomings of the classic models persist in today’s NNMIs: (1) they struggle with generalization across sparse paradigms, (2) they are prone to over-irregularization, and (3) they do not follow child acquisition trajectories. The persistence of these issues suggests that they reflect inherent or “innate” characteristics of NNMIs as a class which are distinct from the characteristics of human language learners. As such, even substantial practical improvements do not necessarily entail increased cognitive plausibility.
Funding source: National Science Foundation Graduate Research Fellowship Program
Award Identifier / Grant number: Graduate Student Research Fellowship
Funding source: Stony Brook University Institute for Advanced Computational Science
Award Identifier / Grant number: Graduate Student Fellowship
Acknowledgments
S.P. gratefully acknowledges funding through the Institute for Advanced Computational Science (IACS) Graduate Research Fellowship and the National Science Foundation (NSF) Graduate Research Fellowship Program under NSF Grant No. 2234683. Some experiments were performed on the SeaWulf HPC cluster maintained by RCC and IACS at Stony Brook University and made possible by NSF Grant No. 1531492. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IACS or the NSF.
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