Whether neural-net models can learn minoritydefault patterns has been a matter of some controversy. Results based on modeling real human language data are hard to interpret due to complexity. Therefore, we examine the learning of a simple artificial language pattern involving defaults using three computational models”:" an Encoder-Decoder RNN, a Transformer Encoder, and a Logistic Regression. Overall, we find that the models have the hardest time with minority defaults, but can eventually learn them and apply them to novel words (although not always extend them to completely novel segments or novel CV-sequences). Typefrequency has the largest effect on learning in all models, trumping the effect of distribution. We examine the weights of two models to provide further insights into how defaults are represented inside the models.

Natural language reduplication can pose a challenge to neural models of language, and has been argued to require variables (Marcus et al., 1999). Sequence-to-sequence neural networks have been shown to perform well at a number of other morphological tasks (Cotterell et al., 2016), and produce results that highly correlate with human behavior (Kirov, 2017; Kirov & Cotterell, 2018) but do not include any explicit variables in their architecture. We find that they can learn a reduplicative pattern that generalizes to novel segments if they are trained with dropout (Srivastava et al., 2014). We argue that this matches the scope of generalization observed in human reduplication.