The Lost Combinator
Mark J. Steedman · Computational Linguistics · 2018
Let me begin by thanking the Association for Computational Linguistics and its Executive Committee for conferring on me the great honor of their Lifetime Achievement Award for 2018, which of course I share with all the wonderful students and colleagues that have made many essential contributions to this work over many years.At the heart of the work that I have been pursuing over my research lifetime so far, whether in parsing and sentence processing, spoken language understanding, semantics, or even in musical understanding by machine, there lies a theory of natural language grammar that brings parsing, compositional semantics, statistical modeling, and logical inference into the closest possible relation. This theory of grammar is combinatory, in the sense that its operations are type-dependent and restricted to strictly string-adjacent phonologically or graphologically-realized inputs, and categorial, in the sense that those operands pair a syntactic type with a type-transparent semantic representation or logical form.I’d like to use this opportunity to briefly address three questions that revolve around the theory of grammar, both combinatory and otherwise. The first question concerns the way that Combinatory Categorial Grammar (CCG) was developed with a number of colleagues, over a number of stages and in slightly different forms. The second is an essentially evolutionary question of why natural language grammar should take a combinatory form. The third question is that of what the future holds for CCG and other structural theories of grammar in computational linguistics and NLP in the age of deep learning.I have called this talk “The Lost Combinator” in homage to the Victorian era poem “The Lost Chord,” in the hope of suggesting that the theoretical development of CCG has always been empirical, rather than axiomatic, in search of the simplest explanation of the facts of language, rather than for confirmation of linguistic received opinion, however intuitively salient.In the late 1960s (when I was a psychology undergraduate at the University of Sussex under Stuart Sutherland, and then started as a graduate student in artificial intelligence at Edinburgh under Christopher Longuet-Higgins), a broad community of theoretical linguists, psychologists, and computational linguists saw themselves as all working on the same problem, under the definition provided by the “transformational” theory of grammar proposed by Chomsky (1957, 1965), using theories of psycholinguistic processing, language acquisition, and language evolution proposed by Lashley (1951), Miller, Galanter, and Pribram (1960), Miller (1967), and Lenneberg (1967), theories of natural language semantics proposed by Carnap (1956), Montague (1970), and Lewis (1970), and computational models of parsing such as those proposed by Thorne, Bratley, and Dewar (1968) and Woods (1970). (I myself was so convinced that this program would succeed that I believed it was time to apply the same methods to other cognitive faculties, taking as my research project for Ph.D. their application to the interpretation of music by machine, following the lead of Max Clowes [1971] in machine vision.)Almost immediately, this consensus fell apart. First, Chomsky himself was among the first (1965) to recognize that transformational rules, though descriptively revealing, were so expressive as to have little explanatory force, and required many apparently arbitrary constraints (Ross 1967). Second, psychologists realized that psycholinguistic measures of processing difficulty of sentences bore almost no relation to their transformational derivational complexity (Marslen-Wilson 1973; Fodor, Bever, and Garrett 1974). Finally, computational linguists attempting to implement transformational grammars as parsers realized that they were spending all their time implementing even more constraints on rules, in order to limit search arising from overgeneration (Friedman 1971; Gross 1978). (Meanwhile, I realized that the problem had not in fact been solved, and returned to natural language processing, thanks to a postdoc at Sussex with Philip Johnson-Laird.)This disillusion wasn’t just a case of internal academic squabbling. There were also a couple of influential reports commissioned by the U.S. and UK governments that ended funding for machine translation (MT) and artificial intelligence (AI) (Pierce et al. 1966; Lighthill 1973). As a result of the second of these reports, which determined that AI was never going to work, PhDs in artificial intelligence like my classmate Geoff Hinton and myself spent ten years or so after graduation in psychology departments (in my case, at the Universities of Sussex and Warwick), until yet another report said AI was working after all and that Britain and the U.S. were falling behind Japan in this vital area. As a result, I could get hired again in computer science, first briefly back at Edinburgh, and then at the University of Pennsylvania (I learned a lesson from this odyssey that I have tried to remember whenever I have been appointed to a committee to report on anything, which is that while reports very rarely do any good, they can very easily do a great deal of harm.)Meanwhile, as a result of these conflicts, the scientific study of language fragmented. The linguists swiftly abjured any responsibility for their grammars (“Competence”) bearing any relation to processing (“Performance”). Because the psychologists could hardly abandon Performance, they in turn became agnostic about grammar, retreating to context-free surface grammar (which they tended to refer to as “parsing strategies”), or a touchingly optimistic belief in its emergence from neural models. Meanwhile, the computational linguists (whose machines were growing exponentially in size and speed from the 16K byte core of the machine that supported the whole group when I started my graduate studies, on to levels that would soon permit parsing the entire contents of the then embrionic Web) similarly found that very little of what the linguists and psychologists cared about was usable at scale, and that none of it significantly improved overall performance over very much simpler context-free or even finite-state methods that the linguists had shown to be incomplete. The reason of course was Zipf’s law, which means that the events with respect to which the low-level methods are incomplete are off in the long tail.It also became apparent to a few computationalists working on speech, MT, and information retrieval that the real problem was not grammar but ambiguity and its resolution by world-knowledge, and that the solution lay in probabilistic models (Bar-Hillel 1960/1964; Spärck Jones 1964/1986; Wilks 1975; Jelinek and Lafferty 1991) (although it was not immediately apparent how to combine statistical models with grammar-based systems without making obviously false independence assumptions).Nevertheless, as any red-blooded psychologist had always insisted, the divorce between competence and performance that everyone else had accepted did not make any sense. The grammar and the processor had to have evolved in lock-step, as a package deal, for what could be the evolutionary selective advantage of a grammar that you cannot process, or a parser without a grammar?It seemed equally obvious that surface syntax and the underlying semantic or conceptual representation must also be closely related, since the only reasonable basis for child language acquisition that has ever been on offer is that the child attaches language-specific grammar to a universal conceptual relation or “language of mind” (Miller 1967; Bowerman 1973; Wexler and Culicover 1980). It seemed to follow that radically new theories of grammar were needed.Theoretical linguists agree that the central problem for the theory of grammar is discontinuity or non-adjacent dependency between predicates and their arguments:Chomsky described discontinuity in terms of movement, which was known to be formally very unconstrained. By contrast, the ATN parser used in the LUNAR project (Woods, Kaplan, and Nash-Webber 1972) reduced all discontinuity to local operations on registers (Thorne, Bratley, and Dewar 1968; Bobrow and Fraser 1969; Woods 1970).In particular, unbounded wh-dependencies like the above were handled by: (a) putting a pointer into a * or HOLD register as soon as the “which” was encountered without regard to where it would end up; and (b) retrieving the pointer from HOLD when the verb needing an object “had” was encountered without regard to where it had started out. (It also included an ingenious mechanism for coordination called SYSCONJ, which one finds even now being reinvented on an almost yearly basis—cf. Woods [2010].) A * register was also used for wh-constructions within a systemic grammar framework by Winograd (1972, pages 52–53) in his inspiring conversational program SHRDLU.However, it was unclear how to generalize the HOLD register to handle the multiple long-range dependencies, including crossing dependencies, that are found in many other languages. In particular, if the HOLD register were assumed to be a stack, then the ATN becomes a two-stack machine (since we are already implicitly using one stack as a PDA to parse the context-free core grammar).On the computational side at least, the reaction to this impass took two distinct forms. Both reactions took the form of trying to reduce the two major operators of the transformation theory, substitution of immediate constituents, or what is nowadays called “Merge,” and “Move,” or displacement of non-immediate constituents, to one. On the one hand, Lexical Functional Grammar (Bresnan and Kaplan 1982) and Head-driven Phrase Structure Grammar (Pollard and Sag 1994) followed Kay (1979) in making unification the basis of movement and merger. Because unification can pass information across unbounded structures, this can be thought of as reducing Merge to Move.On the other hand, Generalized Phrase Structure Grammar (Gazdar 1981), Tree Adjoining Grammar (TAG; Joshi and Levy 1982), and Combinatory Categorial Grammar (CCG, Ades and Steedman, 1982) sought to reduce Move to various forms of local merger. In particular, the latter authors suggested that the same stack could be used to capture both long-range dependency and recursion in CCG.1Natural language grammar exhibits discontinuity because semantically language is an applicative system. Applicative systems (such as programming languages) support the twin notions of: (a) Application of a function/concept to an argument/entity; and (b) Abstraction, or the definition of a new function/concept in terms of existing ones.Language is in that sense inherently computational. It seems to follow that linguistics is (or should be) inherently computational as well. (Of course, it does not follow that computationalists have nothing to learn from linguistics.)There are two ways of modeling abstraction in applicative systems: Taking abstraction itself as a primitive operation (λ-calculus, LISP):(2)a.fatherEsau⇒Isaacb.grandfather=λx.father(fatherx)c.grandfatherEsau⇒Abrahamor Defining abstraction in terms of a collection of operators on strictly adjacent terms aka Combinators, such as function composition (Combinatory Calculus, MIRANDA).(3)b′.grandfather=BfatherfatherThe latter does the work of the λ-calculus without using any variables.Despite the resemblance of the “traces” (or copies) and “operators” (or complementizer positions) of the transformational theory to the λ-operators and variables of applicative systems of the first kind, natural language actually seems to be a system of the second, combinatory kind. The evidence stems from the fact that natural language deals with all sorts of fragments that linguists do not normally think of as semantically typable constituents, without the use of any phonologically realized equivalent of variables, such as pronouns:(4)a.Give[Anna books]?and[Manny records]?b.(Mother to child): There’s adoggie![Youlike]?#the doggie.c.Food that you must[washVP/NP[before eating](VP∖VP)/NP]?.d.ik denk dat ik1Henk2Cecilia3[zag1leren2zingen3]?These fragments are diagnostic of a Combinatory Calculus based on Bn, T, and the “duplicator” Sn, plus application (Steedman 1987; Szabolcsi 1989; Steedman and Baldridge all dependencies, such as and so logical form. syntactic are operators over phonologically realized and their logical forms. are restricted by a Combinatory which in they cannot the already in the language-specific but must be with and project the such language-specific information is in the The combinatory like composition are and such as and are to be over the 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It is that of be to the of the of the long like and work in do they work in In particular, can they learn all the syntactic in the long like and crossing in a way that support semantic they are not actually but are a finite-state or a then by on as for natural language processing, we are in of of the computational linguistic project of also computational of language and if we that like is a real and that learn their first language by of the sentences of their language the of the universal language of we the of what that universal semantic language not get an to that question we can above using and and such as as for the language of to use machine for what it is such variables and their for use in a natural language work was supported in by a Award and a a University of Edinburgh and my and the and all my and students over many