A position note on natural language understanding and artificial intelligence

A position note on natural language understanding and artificial intelligence

Cognition, 10 (1981) 337-340 @ Elsetier Sequoia &A., Lawanne - Printed in The Netherlands 337 A position note on natural language understanding and ...

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Cognition, 10 (1981) 337-340 @ Elsetier Sequoia &A., Lawanne - Printed in The Netherlands

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A position note on natural language understanding and artificial intelligence YQRICKVJILKS” University

of Essex

What .follows is in no sense a ,logical questions I am agnostic, though admitting that, whatever dislalmers their promake/ all artificial intelligence (AI) in fact incorporate example, even th3se at what one might call the “engineering” spectrum of AI research disclaim all theory-tend nonetheless to construct English, say, that from left-to-right, suggest that rightleft processing certain constructions explain such a preference. towards that end of the spectrum myself, opposite which I tend to identify systems independent of their instantiation in machines, humans or animaZs. This is an AI version of linguistic competence theory, and no m3re appealing for that: it is liable to miss the distinctively AI insights that come precisely from consideration of processing constraints. But, I repeat, whatever an .\I worker’s chosen place on that spectrum, doing psychology is not his job. Whether one calls it division of academic la bour, or mere trades unionist protectionism, I believe psychological speculation and testing is best left in the hands of psychologists. Some fifteen years ah, I, began to publish papers on programs to “parse English text semantically”, an enterprise that has been widely misunderstood since then: as, for example, claiming that such a program could not take account of the (syntactic) fact that, say, English determiners tend to occur to the left of adjectives. This was clearly a misunderstanding; no more was intended by the original claim than that the work of parsing English can be done via a structure that is plausibly semantic in nature, with no autonomous syntactic component. This claim still seems to me plausible and proof against Katnian knock-down answers in terms of arms and weapons having “the same meaning” while be;ng associated with different syntactic features. ---

*Reprint requests should be sent to Y. Wilks, Department of Language 8s Linguistics, University of Essex, Wivenhoe Park, Cokhester CQ4 3 SQ, England.

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Y. Wilks

I feel under no obligation to agree that they “have the same meaning” and, moreaver,, it now seems to me that the recent emphasis on “perspectives” in AI 6.a~in Bobrow and Winograd’s HRL language) can offer a formalism in wlmichto make such a position concrete: the knowledge and meaning structures of armr and weapons could plausibly differ if only one of them could “seen from the perspective”’ of countability. Let me restate the principles behind that fifteen-year old approach to “semantic parsing”: (a) that language is fundamentally a linear, segmentable, phenomenon, even at the semantic level, rather thau a hierarchical one; (b) semantic and knowledge-structure dictionary entries are the fundamental data structures for parsing, but there are items in the system (“meaning skeletons”’ for phrases and clauses) that are not reducible to dictionary entries: I called these temp2ates. In brief, this approach does not accept a simple version of the Fregean “principle of compositionality”. (c) there is a very general algorithm for selecting fillers for slots, and hencp overall structures of sentences and texts: I called it “preference”. It was based on a notion of semantic coherence-in short, accepting the semantically densest reading-and never rejected readings, only preferred some to others. (It was, in a clear sense, the opposite of what is known as “constraint analysis”: that alttznatives are counted out not in). These principles still seem to be broadly correct. and I take encouragement not only from, say, recent work on the key role semantically coherent noun groups play in garden path sentences and hence in parsing generally, but also from the general drift of linguistic theory in the last ten years. By that I mean the move towards a more surface-orientated syntax, concerned with th.e role of dictionary entries and slot fiig rather than transformations. If it be replied that one who argues for “semantic parsing” can hardly be cheered by a move towards a surface syntax, I demur. Semantic parsing of the type I intend is deep& superficial. until extended by inference structures and knowledge bases, and I would maintain that even against those who have done the same kind of parsing as that I advocate and called it “conceptual” and “deep”. I have many times pointed out in print its relentlessly, and in my view quite correct, superficial properties. The key question will be the ability of those now engaged in syntactic parsing, such as Marcus, to produce interesting and significant generalizations not

reducible to semantic ones. We are hovering near an old problem: th;tt of semantic primitives. I think it is now clear that there are a lot of b.ad ways of defending such entities: as part of an innate brain language, for example. That does not mean that there are not good ways, and I think one of those: is to be found near the phrase “I%oceduraISemantics”. I have attacked a number of expositions of this no-

Natumllanguageimierstanding and art@kkl inte&ence

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tion, but I think it still offers a potential coup to AI: the construction of a distinctive theory of meaning (which would, ambulando, give procedural meaning to such items as semantic primitives) which was formal and defensible while not being, at bottom, reducible to model theoretic semantics, verificationism, or the banality of brain (or machine) hardware. Another good AI idea that still has much to offer, I believe, is frames, or scripts. I have never accepted that these still insufficiently defined items can guide parsing or be proto-text grammars. The evidence is too strong that we can do what is needed, in initial parsing at least, with weaker structures. Where I do believe them essential, however, is in the stage that immediately succeeds initial parsing. Only with their aid, I have argued, can we make sense of the very simplest utterances that break what I would call preference restrictions. Many such phenomena would be called syntactic by others, but no matter. On my view John ran a mile breaks the pre-reference of run for no object, and is, in that sense, preference breaking or metaphoric. Even such trivial cases can, I believe, be profitably subsumed under a general pattern-matching algorithm (forwhich I have specified a sample version) %iiatmatches preferencebreaking items (the above as much as “my car dzinks gasoline”) against fi-amelike structures to determine an interpretation via what is normally the case for the mentioned entities: gas is normally used by a car (so that interpretation is substituted in a text representation of the sentence), just as running normally extends a distance such as a mile (with corresponding effects on the representation). This sort of approach is, I believe, the appropriate use of frame-like stru= tures (incorporating detailed factual knowledge about the world) in the oralysis of sentences and texts, given a very general assumption that language is inherently, not incidentahy, metaphoric or boundary breaking, and that human comprehension is mapped by a general knowledge-based procedure imposing top-down coherence on what we read and hear. One final area of research, now booming in AI, should be mentioned: the analysis of conversation in terms of plan structures, beliefs and perspectives, loosely what has been known in philosophy and linguistics as speech acts. “Speech acts are dead” said an eminent linguist to me the other da;, which I would interpret bysayingthat philosophyandlinguistics encountered problems with the notion that their theoretical machineries did not allow them to solve. In the case of philosophy, the assumption of an irreducible and knowableby-others notion of intention seems intractable for any procedural account. In the case of linguistics, the machinery of generative grammar made it impossible to bring belief structures (particularly of different individuals, including their beliefs about each other) to bear on the “analysis of utterances.

I believe AI work in this area will be very fruitful and am actively concerned with it myself: partly because the notion of plans can assimilate speech to non-linguistic action in the $vayAustin originally intended, and partly because the perlocutionary effect-the goal of the act of uttering-can be kept firmly in view in an AI account, whereas it tends to get lost in accounts where the goal of speaking seems only to be understood, rather than to achieve some concrete end. I do not believe that such AI work will justify any particular theory of speech acts; on the contrary, I anticipate that the existing philosophical distinctions and tern.inology will disappear, or, at best survive as primitives of system organization. There might well be a group of rules clustered under the label THREAT or PROMISE in a useful system of conversational analysis of t!;e future or, on the other hand, the rule taxonomy used might have no commonsense interpretation. I hope devoutly for the former outcome, bc t we shall see. One danger I see to theoretically interesting natural language analysis in AI is the trend to expert systems: on that view, language about car repair or electrical circuits, say, becomes no more than a side-effect (the word is much used) in a system that plans such activities. This would be a parody world of Wittgensteitian linguistics, and some AI workers, seeing the effect of this trendsatisfying as it may be to commercial interests and Government sponsors -may soon come flocking back to the shelter afforded by the skirts of “generalized competence” and an “innate language faculty”! i ’