TITLE:		PROVIDING INTELLIGENT LANGUAGE FEEDBACK
		FOR AUGMENTATIVE COMMUNICATION USERS
AUTHORS:	Christopher A. Pennington
COMMENTS:	From the Proceedings of the Workshop on Developing AI
		Applications for Disabled People (IJCAI '95)
KEYWORDS: 	augmentative communication, natural language processing,
		compansion, literacy

ABSTRACT:
People with severe speech and motor impairments (SSMI) sometimes use
augmentative communication devices to help them communicate. While
these devices can provide speech synthesis or text output, the rate of
communication is typically very slow. Consequently, augmentative
communication users often develop telegraphic patterns of language
usage. A natural language processing technique termed compansion
(COMPression-expANSION) has been developed that expands uninflected
content words (i.e., compressed or telegraphic utterances) into
syntactically and semantically well-formed sentences.

While originally designed as a rate enhancement technique, compansion
may also be viewed as a potential tool to support English literacy for
augmentative communication users. Accurate grammatical feedback from
ill-formed inputs would be very beneficial in the learning
process. However, the problems of dealing with inherently ambiguous
errors and multiple corrections are not trivial.  This paper proposes
the addition of an adaptive user language model as a way to address
some of these difficulties. It also discusses a possible
implementation strategy using grammatical mal-rules for IPG
(Intelligent Parser/Generator), a prototype system that uses the
compansion technique.