Arguments for a Non-Transformational Grammar

For the past decade, the dominant transformational theory of syntax has produced the most interesting insights into syntactic properties. Over the same period another theory, systemic grammar, has been developed very quietly as an alternative to the transformational model. In this work Richard A. Hudson outlines "daughter-dependency theory," which is derived from systemic grammar, and offers empirical reasons for preferring it to any version of transformational grammar.

The goal of daughter-dependency theory is the same as that of Chomskyan transformational grammar—to generate syntactic structures for all (and only) syntactically well-formed sentences that would relate to both the phonological and the semantic structures of the sentences. However, unlike transformational grammars, those based on daughter-dependency theory generate a single syntactic structure for each sentence. This structure incorporates all the kinds of information that are spread, in a transformational grammar, over to a series of structures (deep, surface, and intermediate). Instead of the combination of phrase-structure rules and transformations found in transformational grammars, daughter-dependency grammars contain rules with the following functions: classification, dependency-marking, or ordering.

Hudson's strong arguments for a non-transformational grammar stress the capacity of daughter-dependency theory to reflect the facts of language structure and to capture generalizations that transformational models miss. An important attraction of Hudson's theory is that the syntax is more concrete, with no abstract underlying elements.

In the appendixes, the author outlines a partial grammar for English and a small lexicon and distinguishes his theory from standard dependency theory. Hudson's provocative thesis is supported by his thorough knowledge of transformational grammar.

Hudson's strong arguments for a non-transformational grammar stress the capacity of daughter-dependency theory to reflect the facts of language structure and to capture generalizations that transformational models miss.