Translation, Brains and the Computer

1 Introduction.- &nbsp;2&nbsp; 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:&nbsp; 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:&nbsp; 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:&nbsp; 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:&nbsp; 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&nbsp; Language.- SAL Nouns.- SAL Verbs.- SAL Adjectives.- SAL Adverbs.<p></p>