Predictive structure or paradigm size? Investigating the effects of i-complexity and e-complexity on the learnability of morphological systems

Abstract

Research on cross-linguistic differences in morphological paradigms reveals a wide range of variation on many dimensions, including the number of categories expressed, the number of unique forms, and the number of inflectional classes. However, in an influential paper, Ackerman & Malouf (2013) argue that there is one dimension on which languages do not differ widely: in predictive structure. Predictive structure in a paradigm describes the extent to which forms predict each other, called i-complexity. Ackerman & Malouf (2013) show that although languages differ according to measure of surface paradigm complexity, called e-complexity, they tend to have low i-complexity. They conclude that morphological paradigms have evolved under a pressure for low i-complexity, such that even paradigms with very high e-complexity are relatively easy to learn so long as they have low i-complexity. While this would potentially explain why languages are able to maintain large paradigms, recent work by Johnson et al. (submitted) suggests that both neural networks and human learners may actually be more sensitive to e-complexity than i-complexity. Here we will build on this work, reporting a series of experiments under more realistic learning conditions which confirm that indeed, across a range of paradigms that vary in either e- or i-complexity, neural networks (LSTMs) are sensitive to both, but show a larger effect of e-complexity (and other measures associated with size and diversity of forms). In human learners, we fail to find any effect of i-complexity at all. Further, analysis of a large number of randomly generated paradigms show that e- and i-complexity are negatively correlated: paradigms with high e-complexity necessarily show low i-complexity. These findings suggest that the observations made by Ackerman & Malouf (2013) for natural language paradigms may stem from the nature of these measures rather than learning pressures specially attuned to i-complexity.

From characters to words: the turning point of BPE merges

“…However, note that there is some evidence from language learning experiments -with neural networks and human participants -which suggests that both are more sensitive to E-complexity than I-complexity (Johnson et al, 2020). It is an open question how our word-internal predictability measures relate to language learning.…”

Inflectional predictability and prosodic morphology in Pitjantjatjara and Yankunytjatjara

“…We make no claims about what speakers or hearers actually do in reality, rather, we are concerned with predictability as a property of the system itself; further experimental research would be required to understand how speakers actually solve the PCFP in Pitjantjatjara (see e.g. Parker, 2018;Johnson, Gao, et al, 2020).…”