Abstract
A number of experiments have demonstrated what seems to be a bias in human phonological learning for patterns that are simpler according to Formal Language Theory (Finley and Badecker 2008; Lai 2015; Avcu 2018). This paper demonstrates that a sequence-to-sequence neural network (Sutskever et al. 2014), which has no such restriction explicitly built into its architecture, can successfully capture this bias. These results suggest that a bias for patterns that are simpler according to Formal Language Theory may not need to be explicitly incorporated into models of phonological learning.
Highlights
Formal Language Theory (FLT; Chomsky 1956) describes how complex a pattern is in terms of the computational machinery needed to represent it
While none of the patterns I investigate have dependencies that are long enough to be affected by this phenomenon, Gated Recurrent Units (GRU) units are relatively standard in the Seq2Seq literature and I leave it to future work to see whether they are necessary for capturing the results presented here
Majority Rule Harmony is a pattern predicted by some constraintbased theories of assimilation in which the number of segments in a word’s underlying representation (UR) with a particular feature value determines what the value of that feature will be throughout the surface representation (SR) of the word (Lombardi 1999; Bakovic 1999)
Summary
Formal Language Theory (FLT; Chomsky 1956) describes how complex a pattern is in terms of the computational machinery needed to represent it. Later experimental work went on to show that people were biased against learning some Subregular patterns (Lai 2015; Avcu 2018; McMullin and Hansson 2019), providing evidence that the phonological grammar might be limited to even simpler levels of the FLT hierarchy, such as those that can be characterized as Strictly Local and Tier-based Strictly Local (TSL; Heinz et al 2011).1 The former level of complexity includes any pattern that bans a finite set of substrings from occurring in a word, while the latter does so over a tier of segments (i.e., certain segments can be ignored by the pattern). Directional harmony mappings like this are Subregular, since determining how a vowel will surface only depends on local information in the input and [ 73 ]
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