How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection
This addresses the debate on whether neural networks can model human-like language learning, though it is incremental in evaluating transformers for cognitive modeling.
The study trained transformer models on English past tense inflection to assess their ability to learn quasi-regularities like humans, finding they achieved high accuracy on unseen regular verbs and some on irregulars, but their performance did not align well with human data.
There is an ongoing debate on whether neural networks can grasp the quasi-regularities in languages like humans. In a typical quasi-regularity task, English past tense inflections, the neural network model has long been criticized that it learns only to generalize the most frequent pattern, but not the regular pattern, thus can not learn the abstract categories of regular and irregular and is dissimilar to human performance. In this work, we train a set of transformer models with different settings to examine their behavior on this task. The models achieved high accuracy on unseen regular verbs and some accuracy on unseen irregular verbs. The models' performance on the regulars is heavily affected by type frequency and ratio but not token frequency and ratio, and vice versa for the irregulars. The different behaviors on the regulars and irregulars suggest that the models have some degree of symbolic learning on the regularity of the verbs. In addition, the models are weakly correlated with human behavior on nonce verbs. Although the transformer model exhibits some level of learning on the abstract category of verb regularity, its performance does not fit human data well, suggesting that it might not be a good cognitive model.