Path: utzoo!attcan!uunet!zephyr.ens.tek.com!uw-beaver!cornell!oravax!daryl
From: daryl@oravax.UUCP (Steven Daryl McCullough)
Newsgroups: comp.ai
Subject: Re: What Has Traditional AI Accomplished?
Summary: Language generation is much more successful than language
	 understanding.
Message-ID: <1712@oravax.UUCP>
Date: 11 Oct 90 20:14:30 GMT
References: <69367@lll-winken.LLNL.GOV> <1990Oct9.184502.106@watdragon.waterloo.edu>
Organization: Odyssey Research Associates, Ithaca NY
Lines: 20

In article <1990Oct9.184502.106@watdragon.waterloo.edu>, cpshelley@violet.uwaterloo.ca 
(cameron shelley) writes:
>   Well, the one [language translation program]
> that I know of is called METEO, which was developed at
> McGill and translates english weather forecasts into french (or was it
> the other way around?)  This may not sound like much, but since the
> National Weather Service Bureau must perform the translation (and over
> a large volume of documents), the fact that about 80% of what METEO
> does needs no correction save them alot of time and money.

The way I understand it, though, language translation for things like
weather reports is being dropped in favor of language generation. In
language generation, the input is data about the weather (or whatever)
in the form of machine-readable charts and so forth, and the output is
a natural language document. By replacing the grammar and semantic
database, one can generate English, French, Innuit, or whatever. This
is much more successful than language translation, since translation
requires a high degree of understanding of the two languages.

Daryl McCullough