A Connectionist Language Generator

Connectionism has been gaining ground as a psychological modeling
technique and shows great promise as a way to build fast, robust
systems to perform intelligent tasks. Connectionism contrasts with
the older tradition, that of explaining intelligent behavior in terms
of the manipulation of complex symbol structures. Without expecting
either style of research to be exclusively correct, it is still
important to seek out the relative strengths and weaknesses of each.
The partisans of the two camps have leapt to the challenge; the
controversy has been fierce, as befits the prize-acceptance as the
best technique for understanding people and building intelligent
systems.
Language, the quintessential human activity, is often used as a
touchstone for the two approaches. The traditional approach views
language as composed of symbols and language use as the manipulation
of symbol structures. Connectionists have largely conceded to this,
accepting the idea of structure as an essential component of
language.
This book calls into question this common assumption, that "structure
is necessary for language modeling," by presenting a generator which
produces appropriate natural language utterances without building
structures along the way. It backs up this demonstration by an
analysis of the generation task, which leads to the conclusion that
massively parallel computation and numeric combination of evidence
are in fact intrinsically necessary for generation, and that,
conversely, structure building is computationally awkward.