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
Conclusion
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
Conclusion
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?
Conclusion
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?
Conclusion
7
Some limits on Translation Quality
First Example
Second Example
Other Translation Examples
Balancing the Picture
Conclusion
8
Deep Learning MT and Logos Model
Points of Similarity and Differences
Deep Learning, Logos Model and the Brain
On Learning
The Hippocampus Again
Conclusion
Part II
The SAL Representation Language
SAL Nouns
SAL Verbs
SAL Adjectives
SAL Adverbs.