Machine Translation And Computer Assisted Translation
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Machine translation and computer-assisted translation are new ways of translating that utilize technology. The demand for translations has increased due to factors like the Cold War, cultural independence, and the internet providing universal access to information. The history of machine translation began in the 1930s and involved researchers developing methods using editors and computers to analyze words and convert them between languages. In the 1950s and 1960s, early machine translation programs used bilingual dictionaries and rules for word order but faced issues. Later developments included various machine translation systems in universities, companies, and the European Union from the 1980s onward. Computer-assisted translation tools now help translators through resources like dictionaries, terminology databases, translation memories, and analyzing previous translations.
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This document provides a history of machine translation and computer-assisted translation. It discusses how advances in technology led to increased demand for translation. Early machine translation systems in the 1950s were rule-based and relied on dictionaries, but results were disappointing. Research declined until the 1980s when systems like Systran and commercial demand increased development. While machine translation now accounts for about 1% of the translation market, computer-assisted tools like translation memories and databases are more commonly used by translators to increase efficiency. The document considers the impacts of new technologies on translation work and the skills required of translators.
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The history of machine translation began in the 1950s with early experiments translating Russian sentences to English. The Georgetown experiment in 1954 was a success, translating over 60 sentences and sparking significant funding for machine translation research. However, fully automatic, high-quality translation of unrestricted text remains an unsolved problem today. Early proposals in the 1930s involved paper tape dictionaries and dealing with grammar between languages. Research in the US began in earnest in the 1950s and operational systems in the 1960s produced poor quality output but met basic needs for speed. The ALPAC report in 1966 found machine translation was more expensive and slower than humans, slowing research progress. Demand grew in the 1970s for low-cost systems, and
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This document provides an overview of machine translation and computer-aided translation. It begins with a brief history of machine translation, from early conceptualizations in the 17th century to modern statistical and neural approaches. It then discusses different types of human-machine interactions for translation, including fully automated, human-aided, and machine-aided models. The document also covers components of computer-aided translation tools like translation memories, term bases and alignment functions that help improve translator efficiency. It concludes with a hands-on exercise using an online computer-aided translation tool.
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Amharic-Arabic text corpora available for Machine Translation task. Therefore, a small parallel Quranic
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Computational linguistics
Computational linguistics is defined as the scientific study and engineering of language from a computational perspective. It originated in the 1950s with the goal of using computers to automatically translate texts between languages. There are two main approaches: rule-based systems which explicitly encode linguistic rules and data-driven systems which use statistical and machine learning methods on large datasets. Computational linguistics is applied in many areas including machine translation, speech recognition, natural language interfaces, and information extraction from texts.
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Computational linguistics
Computational linguistics originated in the 1950s with a focus on machine translation. It aims to use computers to process and understand human language. There are two main goals - theoretical, to better understand how humans use language; and practical, to build tools like machine translation. It draws from linguistics, computer science, psychology, and logic. Some key applications include spelling/grammar checkers, style checkers, information retrieval systems, and computer-assisted language learning tools. However, natural language processing poses challenges due to issues with phonology, morphology, syntax, semantics, and pragmatics.
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1) Computational linguistics involves using computer science techniques to analyze and process human language both in written and spoken form. The field aims to develop systems that can understand, produce, and have conversations in natural language.
2) Early work in computational linguistics focused on machine translation, but the field grew to include modeling other aspects of language like syntax, semantics, and pragmatics. This allowed for developing systems that go beyond translation to process language more like humans.
3) A famous early program was ELIZA from 1966, which was designed to have natural conversations but actually just followed pattern matching routines to generate responses based on keywords. This demonstrated both promise and limitations of early conversational agents.
Can programming be liberated from the von neumman style
John Backus.
Can programming be liberated from the von neumman style.
Conventional programming languages are growing ever more enormous, but not stronger. Inherent defects at the most basic level cause them to be both fat and weak: their primitive word-at-a-time style of programming inherited from their common ancestor --the von Neumann computer, their close coupling of semantics to state transitions, their division of programming into a world of expressions and a world of statements, their inability to effectively use powerful combining forms for building new programs from existing ones, and their lack of useful mathematical properties for reasoning about programs.
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