General-over-specific Markedness Bias as a Balancing Force in GLA-style Learning

Constraints with overlapping violation profiles cause challenges (cf. Credit Problem) for a GLA-type learner. These are exacerbated when combining stringency scales with no-disagreement constraints, producing a complex, overlapping constraint set. In the context of Finnic vowel pattern typology, I illustrate this problem with a North Estonian learner. Learning from positive evidence only and without a general-over-specific markedness bias, the learner overenthusiastically demotes general markedness constraints in favour of specific ones that are coincidentally unviolated by any input form. This produces a non-restrictive final grammar that achieves only a 0.7865 success rate during evaluation. A general-over-specific markedness bias enables the learner to overcome this issue. The bias is implemented as an initial hierarchy of markedness constraint values, calculated from each constraint's rate of application in a sample set of inputs, and can be freely reversed by learning data. Assigning higher initial ranking values to general markedness constraints facilitates acquisition of a more restrictive grammar and therefore allows the GLA to avoid the potential subset problem encountered by a less-restrictive learner. It works in concert with other established biases to enable the learner to successfully acquire the target grammar, increasing the success rate during evaluation to 0.9837.