# A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection

arXiv: https://arxiv.org/abs/2210.12321 · HTML: https://mlsanity.com/paper/2210.12321/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Human Sentence Processing: Recurrence or Attention?](https://mlsanity.com/paper/2005.09471/) (2020)
- [Applying the Transformer to Character-level Transduction](https://mlsanity.com/paper/2005.10213/) (2020)
- [Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate](https://mlsanity.com/paper/1807.04783/) (2018)
- [Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection](https://mlsanity.com/paper/1906.01280/) (2019)
- [Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals](https://mlsanity.com/paper/2005.08826/) (2020)
- [Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection](https://mlsanity.com/paper/1606.00589/) (2016)

## Built on by (most cited first, 2 of 2)

- [Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers](https://mlsanity.com/paper/2410.21013/) (2024)
- [Exploring Linguistic Probes for Morphological Generalization](https://mlsanity.com/paper/2310.13686/) (2023)

## Abstract (arXiv)

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.