The learning of weak noun declension in German - children vs artificial network models
Peter Indefrey, Randy G. Goebel · Max Planck Digital Library · 1993
Different artificial networks are presented with the task of learning weak noim declension in German.This morphological rule is difficult for cue-based models because it requires the resolution of conflicting cue-predictions and a dynamic positional coding due to suffixation.In addition to that its 'task frequency* is very low in natural language.This property is preserved in the training input to study the models' abilities to handle low frequency niles.The performances of three kinds of networks: 1) feedforward networks 2) recurrent networks 3) recurrent networks with short term memory (STM) capacity are compared to empirical findings of an elicitation experiment with 129 subjects of ages 5-9 and adult age.