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.