© 2018

Translation, Brains and the Computer

A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation


  • Addresses fundamental issues to solve the classic problems with machine translation

  • Recounts the little known background of early events affecting the history of machine translation

  • Identifies complexity as principal reason why machine translation has had limited success

  • Illustrates problems of ambiguity and complexity in various present-day machine translation models, rule-based (RBMT), statistical (SMT) and neural MT (NMT)


Part of the Machine Translation: Technologies and Applications book series (MATRA, volume 2)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Part I

    1. Front Matter
      Pages 1-1
    2. Bernard Scott
      Pages 3-11
    3. Bernard Scott
      Pages 13-39
    4. Bernard Scott
      Pages 127-162
    5. Bernard Scott
      Pages 163-171
    6. Bernard Scott
      Pages 173-202
  3. Part II

    1. Front Matter
      Pages 203-203
    2. Bernard Scott
      Pages 205-241

About this book


This book is about machine translation (MT) and the classic problems associated with this language technology. It examines the causes of these problems and, for linguistic, rule-based systems, attributes the cause to language’s ambiguity and complexity and their interplay in logic-driven processes. For non-linguistic, data-driven systems, the book attributes translation shortcomings to the very lack of linguistics. It then proposes a demonstrable way to relieve these drawbacks in the shape of a working translation model (Logos Model) that has taken its inspiration from key assumptions about psycholinguistic and neurolinguistic function. The book suggests that this brain-based mechanism is effective precisely because it bridges both linguistically driven and data-driven methodologies. It shows how simulation of this cerebral mechanism has freed this one MT model from the all-important, classic problem of complexity when coping with the ambiguities of language. Logos Model accomplishes this by a data-driven process that does not sacrifice linguistic knowledge, but that, like the brain, integrates linguistics within a data-driven process. As a consequence, the book suggests that the brain-like mechanism embedded in this model has the potential to contribute to further advances in machine translation in all its technological instantiations.


Computer science with interest in NLP Foreign language departments Language and the brain Semantic processing Semantico-syntactic representation Neural machine translation NMT language acquisition and translation natural language representation

Authors and affiliations

  1. 1.Tarpon SpringsUSA

Bibliographic information

  • Book Title Translation, Brains and the Computer
  • Book Subtitle A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation
  • Authors Bernard Scott
  • Series Title Machine Translation: Technologies and Applications
  • Series Abbreviated Title Machine Translation Tech., Applicat.
  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-3-319-76628-7
  • Softcover ISBN 978-3-030-09538-3
  • eBook ISBN 978-3-319-76629-4
  • Series ISSN 2522-8021
  • Series E-ISSN 2522-803X
  • Edition Number 1
  • Number of Pages XVI, 241
  • Number of Illustrations 55 b/w illustrations, 0 illustrations in colour
  • Topics Natural Language Processing (NLP)
    Computational Linguistics
  • Buy this book on publisher's site
Industry Sectors
IT & Software


“Natural language processing is one of the most rapidly evolving areas of artificial intelligence, and is the subject of this excellent book. … One of the important contributions of this valuable resource is its presentation and comparison of many current state-of-the-art machine translation systems available to the general public. Summing Up: Recommended. Advanced undergraduates through faculty and professionals.” (J. Brzezinski, Choice, Vol. 56 (6), February, 2019)