Translation, Brains and the Computer A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /

• Κύριος συγγραφέας: Scott, Bernard (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Machine Translation: Technologies and Applications, 2
• Θέματα: Natural language processing (Computer science).; Computational linguistics.; Psycholinguistics.; Natural Language Processing (NLP).; Computational Linguistics.

 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. 

 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.