Another Look at the Ranking Problem in OT
Giorgio Magri (MIT)
10:15am, 460-126

Optimality Theory (OT) looks prima facie like a rather exotic combinatorial framework, that does not seem to have any close correspondent within mainstream Learning Theory. For this reason, various scholars are currently exploring variants of standard OT that replace ''strict domination'' with ''additive interaction'', and thus fall within the general class of ''linear models'' very well studied in mainstream Learning Theory. Some recent examples of this line of research are Hayes & Wilson (2008), Coetzee & Pater (2008), Boersma & Pater (2007, 2008)}, Goldwater & Johnson (2003), Potts et al. (2006), etcetera. In this seminar, I will argue that this departure from standard OT is not needed. In fact, I will present a simple trick that allows us to reinterpret within standard OT many results and methods from the theory of linear models. I will illustrate my proposal by concentrating on the case study of algorithms for the Ranking problem that perform both promotion and demotion.