Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.1 6/24/83; site sdamos.UUCP Path: utzoo!linus!decvax!harpo!whuxlm!akgua!sdcsvax!sdamos!elman From: elman@sdamos.UUCP (Jeff Elman) Newsgroups: net.ai Subject: Re: Sastric Sanskrit Message-ID: <19@sdamos.UUCP> Date: Tue, 23-Oct-84 01:46:05 EDT Article-I.D.: sdamos.19 Posted: Tue Oct 23 01:46:05 1984 Date-Received: Fri, 19-Oct-84 05:36:57 EDT References: <12975@sri-arpa.UUCP> Organization: Phonetics Lab, UC San Diego Lines: 77 Rick, Thank you for taking the time to respond to the comments on your original article. I think this discussion reveals some very basic differences in assumptions that one can make, as far as how one should approach the goal of designing an intelligent natural language processor. I'd like to address those basic issues directly. I think they're far more interesting than the question of whether or not Sastric Sanskrit contained ambiguity. At one point you say "Certainly ambiguity is a major impediment to designing an intelligent natural language processor. It would be very desirable to work with a language that allows natural flexibility without ambiguity." Whether or not ambiguity poses an obstacle to building a successful natural language processor depends up what your processor looks like. Don't assume that all architectures have the same problems. That is, I would agree whole-heartedly with you that language understanding systems which are patterned after traditional machine-based parsers find ambiguity to be a serious problem. Such systems also have a lot of difficulty with another, related problem, which is the enormous variability in the acoustic waveforms which represent given phonemes, syllables, or words. I see both problems -- syntactic ambiguity and acoustic variability -- as related because they have to do with instances where the mapping from surface to meaning is complex; and where one has to take other factors into account. I think it is extremely important to point out that in most cases, what one might label as "ambiguous" utterances are -- in their context -- really not at all ambiguous. Similarly, the acoustic variability displayed by (say) a bilabial stop in different phonetic environments does not prevent listeners from recognizing that they heard a bilabial. Human listeners do very well at integrating contextual information into the language understanding process. (Of course, sometimes we do misunderstand each other. But human performance is so much better than machine based systems that it's beside the point.) My conclusion about how to deal with ambiguity or variability is thus different than yours. You say "It would be very desirable to work with a language that allows natural flexibility without ambiguity." I say the alternative is to leave the language alone and work with a language *processor* that is able to take advantage of contextual constraints and has the kind of computational power which is needed to integrate information from large numbers of sources. Serial von Neumann machines do not have this kind of power. If you use them then of course you will be forced into processing only languages with a highly restricted syntax and a minimum of ambiguity. There are many occasions where this kind of limitation is satisfactory, and so that's fine. But I think it's more challenging to accept the ambiguity of natural language as a given, and then to figure out how it is that people (still the only really successfull speech understanders around) resolve that ambiguity. My strong feeling is that this leads you to investigating the sorts of highly interactive, parallel architectures that are being studied here at UC San Diego, at CMU, at Brown, and at other places. Jeff Elman Phonetics Lab, Dept. of Linguistics, C-008 Univ. of Calif., San Diego La Jolla, CA 92093 (619) 452-2536, (619) 452-3600 UUCP: ...ucbvax!sdcsvax!sdamos!elman ARPAnet: elman@nprdc.ARPA