How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection
Neural network models have achieved good performance on morphological inflection tasks, including English past tense inflection. However whether they can represent human cognitive mechanisms is still under debate. In this work, we examined transformer models with different training size to show that: 1) neural models correlate with both human behaviors and cognitive theories' predictions on nonce verbs; and the model with small-size training data that matches parents' input distribution has the highest correlation; 2) neural models make different types of errors on regular and irregular verbs, exhibiting a clear distinction between regulars and irregulars. Therefore, we conclude that neural networks have the potential to be good cognitive models for English past tense.
Code (0)
등록된 구현이 없습니다.
Tasks
Morphological InflectionSimilar Papers 제목 키워드 기반
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection
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 …
Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection
The cognitive mechanisms needed to account for the English past tense have long been a subject of debate in linguistics and cognitive science. Neural network models were proposed early on, but were shown to have clear fl…
DecoderA Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection
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 f…
Morphological InflectionRecurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate
Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and …
DecoderM3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark
As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intell…
Attribute