Natural chunk-and-pass language processing: Just another joint source-channel coding model?

A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the "Now-or-Never" bottleneck. The authors' "Chunk-and-Pass" processing putative...

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Bibliographic Details
Published in:Communicative & integrative biology Vol. 11; no. 2; pp. 1 - 2
Main Author: Clark, Kevin B.
Format: Journal Article
Language:English
Published: United States Taylor & Francis 04.03.2018
Taylor & Francis Ltd
Taylor & Francis Group
Subjects:
Compression
Computer applications
Fault tolerance
fault-tolerance and correction
hierarchical language representation
Language
language coding and decoding
language compression
multilevel lossy code-transmission rate and security
natural language acquisition and pattern duality
Natural language processing
shannon's noisy channel coding and separation theorems
Speech
shannon's noisy channel coding and separation theorems
language coding and decoding
language compression
natural language acquisition and pattern duality
hierarchical language representation
fault-tolerance and correction
multilevel lossy code-transmission rate and security
ISSN:1942-0889, 1942-0889
Online Access:Get full text
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Summary:A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the "Now-or-Never" bottleneck. The authors' "Chunk-and-Pass" processing putatively mitigates the severe multilevel Now-or-Never bottleneck via fast linguistic coding and compression, hierarchical language representation and pattern duality, and incrementally learned item-based predictions useful for grammaticalization over wide spacetime scales. Despite being a promising explanation of language processes, structure, and development, the Chunk-and-Pass model manages the Now-or-Never constraint with seeming reliance on optimal joint source-channel coding, a set of computational attributes for natural and artificial speech based on Shannon's noisy channel theorems. Restating the Now-or-Never bottleneck with information-theoretic source-channel capacity limitations stresses tradeoffs inherent in the authors' model involving multilevel lossy code-transmission rate and security. Such attributes render evolvable associative networks capable of Chunk-and-Pass speech acquisition, recognition, generation, and adaptation, suggesting Chunk-and-Pass processing represents a special case of joint source-channel coding.
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ISSN:1942-0889
1942-0889
DOI:10.1080/19420889.2018.1445899