infinOT can execute Optimality Theory tableaux on an infinite candidate set with the help of finite state technology. Details on the implementation and usage can be found in the accompanying presentation and term paper (under doc).
This is the semester project I did for the course "Loanword Phonology" taught by Armin Buch and Marisa Köllner at the University of Tübingen in the summer semester 2017.
Manual construction of Optimality Theory (OT) tableaux, as is still the default procedure in contemporary linguistics, has the deficit of not covering the complete, i.e. infinite, set of candidates. Even though proposals of how to handle the infinite set computationally by using finite state transducers (FSTs) have been around for almost 20 years, current OT software provides no means of checking against infinitely many candidates. I present a new OT implementation based on finite state technology which is able to derive a winner for any input from an infinite candidate set, while being accessible and easy to use.
Ever since its introduction in 1993, Optimality Theory (OT; Prince and Smolensky 2008) has been a popular framework in phonology, being able to successfully explain a range of phonological phenomena. However, most of the work associated with OT, especially the generation of output candidates, is usually done by hand and rarely automated. This leads to many possible candidates being missed: The real set of candidates is in fact infinite, and while the majority of these possible candidates is completely unrelated to the input or even unpronounceable, we cannot speak of a thorough analysis if we are not sure that all of the infinite bad candidates have been dealt with by our constraints. Even worse, concentrating on a handful of “human-generated” examples, it is easy to overlook a likely candidate that outperforms the desired winner under the current order of constraints.
To avoid such a faulty analysis due to missed candidates, Karttunen (1998) suggests to use finite state transducers (FSTs) to generate an infinite candidate set and reduce it to a single winning candidate. An FST is able to encode mappings between an infinite amount of strings and multiple transducers can be combined into a single one which simply yields the correct output for an arbitrary input. Karttunen’s approach has later been improved by Gerdemann and Noord (2000); however, there is no comprehensive implementation of their algorithms yet.
In this paper, I introduce my finite state based Python implementation of an OT tableau that is able to generate an infinite and completely unrestricted candidate set, mark violations of constraints and filter out losing candidates. The final FST is small and fast, and can produce the winner(s) for any input. Intermediate FSTs additionally provide the possibility to view violation marks assigned by any (finite) constraint and watch selected candidates win and lose across the tableau. Straightforward and simple methods and classes enable easy usage, and many options as well as the possibility to define custom generators and constraints provide the means for building complex tableaux.