Speaker: Jeff Heinz (Stony Brook University)
Dates: Wednesday, April 21, 12:30pm-2:00pm EDT, and Thursday, April 22, 12:30pm-2:00pm EDT
Title: Learning Constraints over Representations of Your Own Choosing
Abstract:
In this 2-day workshop, I present a general method for learning constraints of different complexities over different kinds of representations (Chandlee et al. 2019, Lambert et al. in review). The representations can include whatever information you want —- phonological/prosodic/morphological/syntactic features, autosegments, different ordering relations, trees, graphs and so on —- for linguistic objects in any linguistic subfield. It is shown generally how learning all but the simplest logical kinds of constraints require prohibitively enormous resources. In constrast, for common representations invoked for phonological words, learning the simpler logical kinds of constraints (1) is feasible (2) returns constraints which are the kind found in phonotactic patterns in the world’s languages (3) does not require statistics (contra Wilson and Gallagher 2018), and instead (4) succeeds because of the *structure* provided by the logic and the representational choices. These results surprised even me, and I conclude with a discussion distinguishing between *inductive* and *abductive* inference.