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.

Contenu
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.