Translation, Brains and the Computer : A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation / by Bernard Scott.

Format:

Book

Author/Creator:

Scott, Bernard, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Machine translation 2522-8021 ; 2.

Machine Translation: Technologies and Applications, 2522-8021 ; 2

Language:

English

Subjects (All):

Natural language processing (Computer science).

Computational linguistics.

Psycholinguistics.

Natural Language Processing (NLP).

Computational Linguistics.

Local Subjects:

Natural Language Processing (NLP).

Computational Linguistics.

Psycholinguistics.

Physical Description:

1 online resource (XVI, 241 pages) : 55 illustrations.

Edition:

First edition 2018.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

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.

Contents:

1 Introduction

2 Background

Logos Model Beginnings

Advent of Statistical MT

Overview of Logos Model Translation Process

Psycholinguistic and Neurolinguistic Assumptions

On Language and Grammar

Conclusion

3 - Language and Ambiguity: Psycholinguistic Perspectives

Levels of Ambiguity

Language Acquisition and Translation

Psycholinguistic Bases of Language Skills

Practical Implications for Machine Translation

Psycholinguistics in a Machine

4- Language and Complexity: Neurolinguistic Perspectives

Cognitive Complexity

A Role for Semantic Abstraction

Connectionism and Brain Simulation

Logos Model as a Neural Network

Language Processing in the Brain

MT Performance and Underlying Competence

5 - Syntax and Semantics: Dichotomy or Integration?

Syntax versus Semantics: Is There a Third, Semantico- Syntactic Perspective?

Recent Views of the Cerebral Process

Syntax and Semantics: How Do They Relate?

6 -Logos Model: Design and Performance

The Translation Problem

How Do You Represent Natural Language?

How Do You Store Linguistic Knowledge?

How Do You Apply Stored Knowledge To The Input Stream?

How do you Effect Target Transfer and Generation?

How Do You Deal with Complexity Issues?

7 - Some limits on Translation Quality

First Example

Second Example

Other Translation Examples

Balancing the Picture

8 - Deep Learning MT and Logos Model

Points of Similarity and Differences

Deep Learning, Logos Model and the Brain

On Learning

The Hippocampus Again

Part II

The SAL Representation Language

SAL Nouns

SAL Verbs

SAL Adjectives

SAL Adverbs.

Other Format:

Printed edition:

ISBN:

978-3-319-76629-4

9783319766294

9783319766287

9783319766300

9783030095383

Access Restriction:

Restricted for use by site license.