  I survey some recent applications-oriented NL generation systems, and claim
that despite very different theoretical backgrounds, these systems have a
remarkably similar architecture in terms of the modules they divide the
generation process into, the computations these modules perform, and the way
the modules interact with each other. I also compare this `consensus
architecture' among applied NLG systems with psycholinguistic knowledge about
how humans speak, and argue that at least some aspects of the consensus
architecture seem to be in agreement with what is known about human language
production, despite the fact that psycholinguistic plausibility was not in
general a goal of the developers of the surveyed systems.
