Abstract When producing morphologically complex words, speakers can draw on memorized examples of morphological structure that differ in specificity, ranging from word-specific information to broad generalizations. However, there is much that is not known about how memory retrieval operates in morphological production, particularly for languages with agglutinative structure, like Turkish. While it has long been suggested that item-specific memory does not play a significant role in such languages, there is empirical evidence for affix chunking in Turkish. This paper models morphological production as a sequential decision task that can be optimized with reinforcement learning. Applied to Turkish noun inflection, the model predicts a strategy of blending synthesis with multi-affix chunks gleaned from memorized words, but more in some parts of the paradigm than others, suggesting a fine-tuned relationship between synthesis and memory utilization.
Elsner et al. (Thu,) studied this question.