Formal and computational aspects of optimality-theoretic syntax

In this dissertation, I propose a formal framework for stating in detail a class of Optimality-Theoretic models for syntax. I discuss empirical consequences of the key choices in the formalization and investigate computational properties of the models, in particular decidability of the parsing and generation tasks. The candidate analyses I assume are non-derivational, represented as tuples of parallel representation structures whose elements stand in a correspondence relation in the style of Lexical-Functional Grammar (LFG). The assumption of this type of candidates is motivated by learnability considerations and has the advantage that one can exploit formal and computational results for LFG and related grammar formalisms. The formalization I discuss (in chapter 4) builds on Joan Bresnan’s original proposal of casting Optimality-Theoretic Syntax in an LFG setting (OT-LFG). The set of all possible candidates is specified by a formal LFG-style grammar; a particular candidate set is defined as those possible candidates whose functional (f-)structure is subsumed by an f-structure representing the input. OT constraints are specified as structural description schemata using the primitives of LFG. I discuss details of the status of candidates violating Faithfulness constraints, as they are required to derive expletive elements (like English do) and non-overt elements (like in pro-drop). I argue that in OT-LFG, Faithfulness violations can be modelled very naturally as a tension between a candidate’s f-structure and its categorial structure and lexical material. Thus the subsumption-based definition of candidate sets can be kept up without implying an overly restricted candidate generation function Gen; in this formal model all language differences can be viewed as an effect of constraint (re-)ranking. Besides the standard production-based (or expressive) optimization model, I discuss comprehension-based (or interpretive) optimization, in which the terminal string is fixed across the members of the candidate set (chapter 5). Formally, this is only a minor modification of the definition of the candidate set, but there are interesting conceptual and empirical issues concerning parallelism between the two “directions” of optimization, and in particular the combination of both in a bidirectional model. I present a bidirectional account of pro-drop in Italian, which derives a recoverability condition as an effect of the interaction of the two optimizations. Building on computational results for LFG generation, I discuss the processing tasks associated with the two types of uni-directional optimization models and with their combination in a bidirectional system (chapter 6). The two main issues in processing are the control of the infinite candidate set and directionality of processing. I show that generally, the conceptually and empirically well-motivated formalization that I argue for provides a sufficiently restricted v basis for a computational account. While parsing (and generation) with an unrestricted OT Syntax system is undecidable in the general case, decidability is guaranteed if either a recoverability condition based on a finite context representation is assumed, or a specific type of bidirectional model (with strong bidirectionality) is applied.