Link to the paper: here
Abstract: The tier-based strictly local (TSL) languages are a class of formal languages that, alongside the strictly piecewise class, effectively model some long-distance generalizations in natural language (Heinz et al., 2011). Two learning algorithms for TSL already exist: one by Jardine and Heinz (2016) and one by Jardine and McMullin (2017). The former is limited in that it cannot learn all TSL patterns. The latter is restricted to a batch-learning environment.
We present a general algorithm without these limitations. In particular we show that TSL is efficiently learnable online via reinterpretation of a strictly local grammar, and this mechanism generalizes to the strictly piecewise class as well. However we note that the known TSL learning algorithms are not robust in the face of interaction with other constraints, posing a challenge for the utility of this class for phonotactics
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