Maria Gouskova of NYU will present the Linguistics colloquium in N400 of the ILC at 3:30 Friday Sept. 16th.
Learning Nonlocal Phonology
Sounds usually interact locally, but some phonological rules involve segments that are not adjacent. In English, the liquid “l” becomes “r” in “music-al”, “flor-al” vs. “tubul-ar” and “circul-ar” when the preceding syllable contains an “l” (*”tubul-al”, *”lun-ar”). This kind of consonant dissimilation is traditionally analyzed using a special level of representation where only “l” and “r” are present–an autosegmental tier. On this tier, the prohibition against adjacent identical liquids is local. Tiers capture the typological generalization that nonlocal interactions such as dissimilation, vowel harmony, and consonant harmony generally involve segments that belong to a natural class.
Work in inductive learning of nonlocal phonology supplies a learnability argument for tiers: without tiers, the space for possible interactions is unsearchably large, so learners are likely to miss nonlocal interactions without some bias towards tiers (Hayes and Wilson 2008, Goldsmith and Riggle 2012). But we still do not know how learners actually discover tiers. Some proposals approach this problem as a directed graph search: the graph in this case is a pre-set feature-geometric hierarchy of features (Futrell et al. 2015). Others pursue brute-force searches for of the segments that might participate in a restriction, which runs the danger of identifying accidental patterns that people do not notice (Albright and Hayes 2006 et seq.).
The alternative pursued in this talk is learners induce tiers based on observable properties of the language. In some languages, learners can be moved to posit non-local representations to explain affixal alternations that do not have a local explanation. In other languages, the learning of local phonotactics reveals distributional irregularities, which are interrogated further in search of the right non-local representation. This procedure does not assume a universal feature geometry, and it is less likely to be misled by irrelevant exceptionless generalizations.