Adaptor Grammars (AGs) are nonparametric Bayesian models that generalize probabilistic context free grammars (PCFG), and have proven to be successful for unsupervised morphological segmentation, where a PCFG is a morphological grammar that specifies word structure (Johnson, 2008; Sirts and Goldwater, 2013;<cite> Eskander et al., 2016</cite> Eskander et al., , 2018 .