Machine translation: technologies and applications ;
volume 2
Includes bibliographical references.
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 -- On cognitive complexity -- A role for semantic abstraction and generalization -- 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 versus 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 cope with complexity? -- 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 and continual learning -- Conclusion -- Part II -- 9 The SAL representation language -- Overview of SAL -- SAL parts of speech -- SAL nouns (WC 1) -- SAL verbs (WC 2) -- SAL adjectives (WC 4) -- SAL Adverbs (WC 3 and WC 6).
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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.
Springer Nature
com.springer.onix.9783319766294
TRANSLATION, BRAINS AND THE COMPUTER.
3319766287
Machine translating.
Neurolinguistics.
Psycholinguistics.
Translating and interpreting-- Data processing.
Computational linguistics.
FOREIGN LANGUAGE STUDY-- Multi-Language Phrasebooks.
LANGUAGE ARTS & DISCIPLINES-- Alphabets & Writing Systems.
LANGUAGE ARTS & DISCIPLINES-- Grammar & Punctuation.
LANGUAGE ARTS & DISCIPLINES-- Linguistics-- General.