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Introduction
Published in Krzysztof Wołk, Machine Learning in Translation Corpora Processing, 2019
During the 1980s, the forerunner of immediate online translation services was the rule-based Systran system, which originated in France and was limited to the Minitel network [134]. By the mid-1990s, many MT providers were offering Internet-based, on-the-spot translation services. In subsequent years, machine translation technology has advanced immeasurably, with Google translate supporting fifty-seven languages [135]. However, the overall translation quality of online MT services is frequently poor when it comes to languages other than English, French, German and Spanish. On the other hand, improvements are continuously being made. Google and other developers offer an automatic translation browsing tool, which can translate a website into a language of your choice [134]. While these services provide acceptable, immediate, “rough” translations of content into the user’s own language, a particular challenge for MT is the online translation of impure language that is colloquial, incoherent, not grammatically correct, full of acronyms and abbreviations, etc. For obvious reasons, when scientific, medical and technical texts are being translated, the accuracy of the MT is of paramount importance. In general, the use of systems with satisfying quality for a narrow domain (e.g., medical texts) is quite possible.
Natural Language Processing and Translation Using Machine Learning
Published in Abid Hussain, Garima Tyagi, Sheng-Lung Peng, IoT and AI Technologies for Sustainable Living, 2023
Google Translate is a text translation tool provided by Google that is free and multilingual. It has a website interface as well as Android and iOS mobile apps. Developers can use its API to create browser extensions and software apps. Over 100 languages, both living and dead, are supported by Google Translate. As of May 2017, it was serving over 500 million individuals every day. It was translating more than 100 billion words per day as of 2018.
English Translation proofreading System based on Information Technology: Construction of semantic Ontology Translation Model
Published in Applied Artificial Intelligence, 2023
In 2003, YoshuaBengio (Brightman et al. 2021; Dirnagl et al. 2022; Doherty and Buckley 2021) and other scholars criticized the statistical machine translation model. They believed that the core of the statistical machine translation model is to carry out translation by constructing the joint probability function of word sequences in the language. As the number of words increases, the dimension of the joint probability function will also increase. It becomes very difficult to construct joint probability functions. To solve the problem of dimensionality, they propose to analyze and summarize the distribution of words instead of constructing joint probability functions, and propose a language model based on neural networks. That same year, Philipp Koehn (al), a professor in Johns Hopkins University’s computer science department, proposed a new phrase-based translation model and decoding algorithm, and he led the open sourceMoses machine translation tool that became a standard feature of the statistical machine translation era. When Google launched Google Translate in 2006, it used statistical machine translation.