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Probably the most successful accounts o f rdk have been offered within the constraint-based approach o f OT. As we will see, the OT account has now proven superior to that from traditional ‘rule-and-derivation’ theories (‘RDT’) in that it has achieved a higher level o f explanation. OT differs fundamentally from the basic notions o f RDT in several important respects. Critically, there are no rules, and thus no rule ordering relationships, no derivations, no intermediate levels o f representation, and no language-specific restrictions on the set o f underlying representations. Instead, for any given input (or commonly known as underlying form ‘UR’), a ranked set o f universal constraints evaluates in parallel a potentially infinite set o f candidates and selects one as

optimal, i.e. the most ‘harmonic’. The optimal candidate is the one that best satisfies the constraint hierarchy. Constraints ( C o n ) , ideally included in UQ are o f at least two fimdamental and often antagonistic types, namely ‘markedness’ constraints and ‘faithfulness’ constraints. Markedness constraints evaluate the well-formedness o f output candidates, favouring certain structural configurations over otiiers. Faithfulness constraints, on the other hand, demand identity between input and output strings. Unlike other theories in which constraints are inviolable, OT constraints are violable, and to be understood as specific empirical hypotheses about UG that OT posits. The basic components o f OT are: a lexicon which can provide input candidate sets, a ranked set o f violable constraints, and an evaluation function which eliminates non-optimal

Chapter 4 - Rendaku in Optimality Theory

candidates. The core components o f OT are summarized in (35).

(35) Core Components o f the OT grammar

Lex ic o n: contains lex ica l representations (or underlying form s) o f m orphem es (roots, stem s, and affixes) o f a language, including phon ological, m orphological, syntactic, and sem antic properties, w hich form the input to:

Generator: generates output candidates for some input, and submits these to: Evaluator: the set o f ranked constraints, which evaluates output candidates as to their harmonic values, and selects the optimal candidate (phonological surface form, syntactic S-Structure, etc.).

Then, the basic two-layered OT architecture is as shown in (36) below.

(36) Basic OT architecture

input --- ► GEN -► candidates --- ► EVAL --- ► output

A universal and automatic G en receives an input and generates a set o f candidates. Another universal function EVAL applies the language-particular constraint hierarchy

to this candidate set, locating its most harmonic member: the output. Among the core components, the OT conception o f the input is one o f the significant points o f diversion fi*om earlier derivational theories. In pre-OT phonology (and syntax), different forms o f limitations/restrictions, such as lexical/phonological redundancy rules, morpheme structure constraints, or the lexicon itself, are often imposed on input to account for between-language variation such as inventory restrictions. OT, however, assumes that no language-particular restrictions hold at the level o f input; the set o f possible inputs to the grammar is universal and innate. This is one o f the most fimdamental principles o f the theory. In other words, inputs in any language are free to contain any kind o f linguistic primitives (e.g. phonological or morpho-syntactic features) in fi*ee combination. This principle (37) is known as Richness o f the base (‘ROTE’), first

Chapter 4 - Rendaku in Optimality Theory

proposed in Prince and Smolensky (1993):

(37) Richness o f the base:

The source o f all systematic cross-linguistic variation is constraint reranking. In particular, the set o f possible inputs to the grammars o f all languages is the same. The grammatical inventories o f languages are defined as the forms appearing in the outputs that emerge fi*om the grammar when it is fed the universal set o f all possible inputs.

(Prince and Smolensky 1993:191)

This means that the grammar and the lexicon are no longer integrated via devices like redundancy rules, morpheme structure constraints or underspecification. A ll languages share the same set o f potential inputs (= basé). All differences in the inventory o f elements permitted in surface structure must be derived fi*om the interactions o f markedness and faithfulness constraints. ROTB is a natural consequence o f one o f the central and perhaps striking assumptions in OT - a single invariant ranking o f constraints represents the grammar o f each language.

In OT grammar, the leamability burden is reduced to the learning o f the constraint ranking and the establishment o f lexical representations. Constraint ranking is all that the language learner must discover in order to arrive at the target grammar. The issue o f learning constraint hierarchies - leamability o f OT grammar - has been acknowledged and elaborated since the first arrival o f the theory itself. One o f the current views holds that learning is essentially achieved by an algorithm which deduces relevant rankings from cross-comparisons o f constraint violations (rather than satisfaction) between the attested output, namely positive evidence^ and other candidates. Positive evidence consists o f the grammatical forms - optimal outputs o f the grammar - available in the child’s linguistic environment. Yet negative evidence consists o f the ungrammatical forms - suboptimal outputs o f the grammar - normally not available in the primary data. Among various algorithm models is the RCD algorithm developed by Tesar and

Chapter 4 — Rendaku in Optimality Theory

Smolensky (1996)t Ranking and re-ranking always involve constraint demotion, and the demotion must be ‘minimal’ in that a constraint is demoted to a position immediately below the highest-ranking constraint that induces its violation in the optimal output.

The process o f acquisition is presumed to proceed by the re-ranking o f constraints, specifically by the minimal demotion o f markedness constraints (Tesar and Smolensky 1996, 1998, 2000). Another important prediction is that the initial state o f the algorithm is [Markedness > Faithfulness]: default ranking o f markedness constraints over the antagonistic faithfiilness constraints (e.g. Gnanadesikan 1995; Smolensky 1996a, b; Tesar and Smolensky 2000, among others). Various acquisition data suggest that many production errors that occur in early stages o f development have only been characterized by the ranking; yet some researchers argue otherwise, namely [F>M ] (Hale and Reiss 1998).