A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection

• URL: http://arxiv.org/abs/2210.12321v1
• Date: Sat, 22 Oct 2022 00:59:40 GMT
• Title: A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection
• Authors: Adam Wiemerslage and Shiran Dudy and Katharina Kann
• Abstract summary: We study the correlation between human judgments and neural network probabilities for unknown word inflections. We find evidence that the Transformer may be a better account of human behavior than LSTMs.
• Score: 20.977461918631928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology. This debate has gravitated into NLP by way of the question: Are neural networks a feasible account for human behavior in morphological inflection? We address that question by measuring the correlation between human judgments and neural network probabilities for unknown word inflections. We test a larger range of architectures than previously studied on two important tasks for the cognitive processing debate: English past tense, and German number inflection. We find evidence that the Transformer may be a better account of human behavior than LSTMs on these datasets, and that LSTM features known to increase inflection accuracy do not always result in more human-like behavior.

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