General-over-specific markedness bias as a balancing force in GLA-style learning
Published in Supplemental Proceedings of the 2023 and 2024 Annual Meetings on Phonology, 2025
Recommended citation: Vesik, K. (2025). General-over-specific markedness bias as a balancing force in GLA-style learning. In G. Avelino, M. Balihaxi, C. Colvin, V. Czarnecki, H. Joo, C. Wang, U. Zorbarlar, A. Jardine, & A. McCollum (Eds.), Supplemental Proceedings of the 2023 and 2024 Annual Meetings on Phonology. Amherst, Massachusetts: University of Massachusetts Amherst Libraries. https://doi.org/10.7275/amphonology.3032 https://openpublishing.library.umass.edu/amphonology/article/id/3032/
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.