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2018 | OriginalPaper | Buchkapitel

4. Language and Complexity: Neurolinguistic Perspectives

verfasst von : Bernard Scott

Erschienen in: Translation, Brains and the Computer

Verlag: Springer International Publishing

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Abstract

In the previous chapter we dealt with the problem of ambiguity and how simulation of input-driven, psycholinguistic processes enables a semantico-syntactic translation model to deal effectively with ambiguity. In the present chapter we deal with the issue of complexity, focusing in particular on the constraining effect that cognitive complexity has on MT development as it attempts to cope with the ambiguities of langauge. We define cognitive complexity as the difficulty developers experience in maintaining complex systems. We show how the associative nature of Logos Model’s neural-like translation paradigm allows it to deal more effectively with cognitive complexity than is possible with rule-based technology, or indeed any other MT paradigm. We attribute the reasons for this to Logos Model’s serendipitous correspondence to findings of neuroscience on the brain’s processing of language, citing the brain’s evident freedom from complexity in processing language as a motivation for this direction in Logos Model design. We focus on two regions of the brain that are involved with language: (1) the prefrontal temporal cortex designated as the Broca area, commonly connected with rule-based processes, and (2) the hippocampus, a well-defined reticulum in the medial temporal lobe distinguished for its declarative, associative memory processes, and whose connection with language processes has only recently been proposed by neuroscientists. We provide illustrations and examples of how the associative processes of the hippocampus have been simulated in Logos Model, and how Logos Model has benefited from this simulation.

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Fußnoten
1
Cognitive complexity has only indirect connection with complexity science whose interest is in complex systems the behavior of which is not entirely explainable on the basis of system-component interactions. Complexity science may indeed be an issue in MT but the focus here has to do with the cognitive difficulty develops experience in maintaining complex systems.
 
2
Palmer (2006). This author reminds us that Chomsky (1957) attacked Skinner’s associationist perspectives expressed in the latter’s Verbal Behavior, and thereby opened the door to a post-behaviorist era of rule-based linguistics.
 
3
Processing prior to the sequences shown in Fig. 4.1 would have resolved the morpheme table to a noun rather than a verb.
 
4
The words kitchen and table share a common code (307 “eating”) in the 4th edition of Cromwell’s Roget Thesaurus. This shared code constituted a better scoring association between kitchen and the correct sense of table than was found for any other paired association of these two words. Resolution of common nouns remained experimental at Logos however, and never got to be implemented in the commercial product. See Postscript 4-B for all solutions that were being considered for resolving common noun polysemy.
 
5
See Postscript 6-B in Chap. 6 for translations of sentence (9) by Google Translate (both SMT and GNMT), by Microsoft’s NMT Translator, and by Logos Model.
 
6
The absence of semantics in Chomsky’s syntax-first theory obliged him to preclude deterministic parsing by the brain. In Aspects (Chomsky 1965, 21f) he says that an individual’s “internal grammar” unconsciously produces three structural descriptions for the sentence “I had a book stolen.” One sees here the seeds of complexity being sown in Chomsky’s approach to language; one cannot separate syntax and semantics without inviting ambiguities and their attendant complexity effects.
 
7
In Logos Model, pattern-rules number in the tens of thousands. Because patterns are content-addressable, memory store size has markedly sub-linear effect on throughput.
 
8
David G. Hays, MT pioneer (and originator of the term computational linguistics) is known to have identified the application of rules to the input stream as the most troublesome problem confronting MT. His observation was made at a time when MT was driven mostly by algorithms and rules. See Kay and Hays (2000).
 
9
Pinker and Prince (1988) half agree on the role of analogy. They maintain that language use necessarily entails rule-based symbol manipulation, but such rules they say are few in number, supplemented by language patterns based on analogy. Note also Fauconnier (1997, p. 18): “Analogical mapping is so common that we take it for granted. But it is one of the great mysteries of cognition.”
 
10
There would have to be a longer pattern-rule to handle from-to constructions like from the crate to the wall, in which case from would be given a different transfer (viz., от (away from). This longer pattern-rule would preempt the shorter one in (7).
 
11
See image of a canonical cortical circuit in Fig. 4.4.
 
12
Deep learning AI centers around the world are combining so-called “deep” neural net technology with statistical technology to produce machine translation (NMT) models of great promise, as the present book seeks to indicate. This new technology however is more motivated by trends in AI and machine learning than by early connectionist concerns for biological verisimilitude. See Chap. 8 for a fuller discussion of this development.
 
13
Pinker and Prince (1988) criticize connectionism’s weakness vis à vis generalization and the absence of any use of analogy in their models.
 
14
Figure 4.4, originally drawn for internal use in the mid-1990s, appears in Scott (2000, 2003).
 
15
For a fuller depiction of Logos Model connectivity, see Fig. 4.7. See Socher et al. (2011) for description of a non-symbolic recursive neural net. It is clear that Logos Model anticipated recursive nets architecturally, but hardly so in computational respects (See Chap. 8).
 
16
Logos Model’s dealing with common noun polysemy is limited to the use of Subject Matter Codes (SMC) specified at run time. For example, the technical meaning and transfer for the word bug would be selected at run time, assuming the lexicon contained an entry for bug having the technology SMC designation, and that the user specifies this technology SMC at run time. In general, this solution has not proved effective inasmuch as the majority of common nouns are not distinguishable by subject matter. See fuller discussion in Postscript 4-B
 
17
The function of these hidden layers will be discussed in Chap. 6.
 
18
Kurczek et al. (2013), Duff and Brown-Schmidt (2012, web): “… we propose the hippocampus as a key contributor to language use and processing.”; Duff and Brown-Schmidt (2017).
 
19
In our view, a machine’s acquiring of a sentence for translation is akin to human language acquisition. Both have to take in and decode an initially meaningless string of symbols. Note that in machine learning parlance, this acquisition process is called encoding rather than decoding.
 
20
Though the hippocampus is known to be rich in intrareticulum connections (Marr 1971), no one suggests a linear flow from CA1 to CA4. Furthermore, little is known about how individual hippocampal modules relate to language use and processing. Wintzer et al. (2014) and Kumaran et al. (2016), among others, find that CA3 is recruited for associative encoding and recall as a general matter. O’Keefe and Dostrovsky (1971) have correlated individual neuron firings in the CA1 module with rats’ movements to specific locations. Fanselow and Dong (2010) functionally segregate the hippocampus into a cognitive area (dorsal region) and an affective area (ventral region).
 
21
The hippocampal system includes the parahippocampal gyrus, dentate gyrus, entorhinal cortex, and subiculum.
 
22
See Postscript 4-F for further discussion. The differences here between the hippocampus and Broca might be gleaned by what is happening in the first versus the second “take” of a troublesome sentence. The first take pertains to ordinary language processes, the double take to puzzle work that typifies much of neuropsychological testing.
 
23
The present author presciently argued for such a connection in (Scott 1990, 2000). It took almost two decades for the literature to come to the same conclusion (Optiz 2010); Duff, Brown-Schmidt (2012 web): “We conclude that the relational binding…afforded by the hippocampal declarative memory system positions the hippocampus as a key contributor to language use and processing.” See further discussion of this in Postscript 4-G.
 
24
The authors found that the hippocampus became more active in students exhibiting higher proficiency in the foreign language. Students having more difficulty in mastering the language displayed larger gray matter increases in the middle frontal gyrus (general Broca region). This would seem to comport with our impression of Broca as being more analytical in its functioning, virtually the opposite of hippocampal associative binding.
 
25
Optiz and Friederici (2003). This and similar studies found reduced hippocampal involvement in grammaticality judgments, but do not shed any light on hippocampus involvement in ordinary, everyday language use, a matter exceedingly more difficult to study (See Chap. 5 on this).
 
26
Squire and Sola-Morgan (1991, 1380):“[The hippocampus and related structures], presumably by virtue of their widespread and reciprocal connections with neocortex, are essential for establishing long-term memory for facts and events (declarative memory). The medial temporal lobe memory system [hippocampal area] is needed to bind together the distributed storage sites in neocortex that represent a whole memory. However, the role of the hippocampus system is only temporary. As time passes after learning, memory stored in neocortex may gradually become independent of medial temporal lobe structures.” Note that the relevance of the foregoing for language is only by implication, and in any case seems outdated.
 
27
In neuropsychology, explicit knowledge is knowledge that can be recalled and articulated. Explicit knowledge is also known as declarative knowledge, and is the sort of knowledge dealt with in the hippocampus. Declarative knowledge is characterized as a knowing that as opposed to a knowing how. Implicit knowledge (the knowing how in the neocortex) refers to skills, habits (procedures of an automatic nature) that are largely subconscious, a tacit knowledge that is more or less inarticulable. In second language acquisition (L2), one can conjecture that as skills develop, habit may eventually replace the initial, more explicit, declarative role of the hippocampus. The matter is viewed quite differently with first language acquisition (L1) however. DeKeyser (2008) holds that children acquire their native languages through implicit, inductive, essentially inarticulable processes right from the start.
 
28
Recent studies by Verfaellie et al. (2014) and Race et al. (2015) report that even when consolidated elsewhere in the neocortex, semantic memories remain dependent upon the hippocampus for retrieval.
 
29
Obviously it takes the higher cognitive regions of the neocortex for the mind to realize that an utterance like colorless green ideas sleep furiously is capricious.
 
30
A lexical recognition study by Fiebach et al. (2002) found that the Broca area (Brodmann Area 44) is especially activated for low-frequency and pseudowords, whereas high-frequency words activate the middle temporal gyrus (MTG - BA21). The middle temporal gyrus (MTG) is not to be confused with the medial temporal lobe (hippocampus), but studies link MTG to the medial temporal lobe (hippocampus), a link made evident in cases of semantic memory impairment (Chan et al. 2001).
 
31
Bonhage et al. (2015). This recent study showed that in a comprehension exercise, the hippocampus, and regions closely linked to it, handled regular sentences and the Broca area handled nonsense sentences.
 
32
Glaser et al. (2013) show that IFG areas BA44 and BA45 (Broca area) are recruited when embeddings and long-distance dependencies cause comprehension “interference.”
 
33
The two SMT versions of Google Translate and Microsoft’s Bing Translator, not known for parsing, nevertheless both resolved das to who and thus translate (10) correctly. The subsequent NMT versions of these system (plus Logos Model) also translate (10) correctly. Ironically, the two hybridized, rule-based systems we have been testing, PROMT Translator and SYSTRANet, both misresolve das in (10) to the article.
 
34
Duff and Brown-Schmidt (2012, 8): “A compelling approach to addressing these questions is to examine language at the intersection of declarative and non-declarative memory systems and to view the activities of language…as necessitating a division of labor between the memory systems.” This clearly refers to the declarative hippocampus and procedure-based Broca, respectively.
 
35
Kumaran et al. (2016, web): “The hippocampus and the neocortex interact…to support interleaved learning… [However] many types of learning are initially hippocampus dependent.”
 
36
Duff, Brown-Schmidt (2012, 5): “These provocative findings regarding the hippocampal contributions to online [language] processing have profound implications for theories of language processing and use…and should encourage increased interest in the relationship between language and declarative memory.”
 
37
It is a curious circumstance that complexity effects are so little addressed in neuroscience’s discussions of language. A collection of papers entitled Pattern Perception and Computational Complexity, edited by W. Tecumseh Fitch et al. (2012), largely focuses on complexity as a characteristic of different grammars, but not as a general, computational difficulty in the handling of language that needs to be accounted for. One particularly interesting paper in this collection by M. H. de Vries et al. (2012), deals with extreme examples of multiple, long-distance and crossed dependencies, and of the memory “overload” that they engender, illustrating in the authors’ words the “upper limits of both human sequence learning and natural-language processing” (p. 2074). But we still want to learn from neuroscientists their views as to why and how our underlying competence for language handles the complexity potential of everyday language use, language use that so challenges the machine and its MT developers but hardly the brain.
 
38
See Postscript 4-H for translations of (11d) by these systems as an entire sentence.
 
39
Clearly, in the decade since Logos closed its doors, the availability of big data and the emergence of deep learning algorithms have made the problem of sense recognition more tractable by orders of magnitude.
 
41
See observations on single cell memory by Sidiropoulou et al. (2009). See also Crick and Asanuma (1986, 368).
 
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Metadaten
Titel
Language and Complexity: Neurolinguistic Perspectives
verfasst von
Bernard Scott
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-76629-4_4