Machine translation, sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
In linguistics, a corpus or text corpus is a language resource consisting of a large and structured set of texts. In corpus linguistics, they are used to do statistical analysis and hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory.
Computer-aided translation (CAT), also referred to as machine-assisted translation (MAT) or machine-aided human translation (MAHT), is the use of software to assist a human translator in the translation process. The translation is created by a human, and certain aspects of the process are facilitated by software; this is in contrast with machine translation (MT), in which the translation is created by a computer, optionally with some human intervention.
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, and an API that helps developers build browser extensions and software applications. As of April 2022, Google Translate supports 109 languages at various levels, and as of April 2016, claimed over 500 million total users, with more than 100 billion words translated daily, after the company stated in May 2013 that it served over 200 million people daily.
Apertium is a free/open-source rule-based machine translation platform. It is free software and released under the terms of the GNU General Public License.
Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation.
Bitext word alignment or simply word alignment is the natural language processing task of identifying translation relationships among the words in a bitext, resulting in a bipartite graph between the two sides of the bitext, with an arc between two words if and only if they are translations of one another. Word alignment is typically done after sentence alignment has already identified pairs of sentences that are translations of one another.
CMU Sphinx, also called Sphinx for short, is the general term to describe a group of speech recognition systems developed at Carnegie Mellon University. These include a series of speech recognizers and an acoustic model trainer (SphinxTrain).
Transfer-based machine translation is a type of machine translation (MT). It is currently one of the most widely used methods of machine translation. In contrast to the simpler direct model of MT, transfer MT breaks translation into three steps: analysis of the source language text to determine its grammatical structure, transfer of the resulting structure to a structure suitable for generating text in the target language, and finally generation of this text. Transfer-based MT systems are thus capable of using knowledge of the source and target languages.
Microsoft Translator is a multilingual machine translation cloud service provided by Microsoft. Microsoft Translator is a part of Microsoft Cognitive Services and integrated across multiple consumer, developer, and enterprise products; including Bing, Microsoft Office, SharePoint, Microsoft Edge, Microsoft Lync, Yammer, Skype Translator, Visual Studio, and Microsoft Translator apps for Windows, Windows Phone, iPhone and Apple Watch, and Android phone and Android Wear.
Caitra is a translation Computer Assisted Tool, or CAT, developed by the University of Edinburgh. Provided from an online platform, Caitra is based on AJAX Web.2 technologies and the Moses decoder. The web page of the tool is implemented with Ruby on Rails, an open source web framework, and C++.
Rule-based machine translation is machine translation systems based on linguistic information about source and target languages basically retrieved from dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively. Having input sentences, an RBMT system generates them to output sentences on the basis of morphological, syntactic, and semantic analysis of both the source and the target languages involved in a concrete translation task.
Philipp Koehn is a computer scientist and researcher in the field of machine translation. His primary research interest is statistical machine translation and he is one of the inventors of a method called phrase based machine translation. This is a sub-field of statistical translation methods that employs sequences of words as the basis of translation, expanding the previous word based approaches. A 2003 paper which he authored with Franz Josef Och and Daniel Marcu called Statistical phrase-based translation has attracted wide attention in Machine translation community and has been cited over a thousand times. Phrase based methods are widely used in machine translation applications in industry.
Moses for Mere Mortals (MMM) is a free open source software composed of a set of scripts designed to allow the automation of processes for the installation and operation of the Moses Open Source Translation System, a statistical machine translation system.
The following outline is provided as an overview of and topical guide to natural-language processing:
MateCat is a web-based computer-assisted translation (CAT) tool. MateCat is released as open source software under the Lesser General Public License (LGPL) from the Free Software Foundation.
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
The EuroMatrixPlus is a project that ran from March 2009 to February 2012. EuroMatrixPlus succeeded a project called EuroMatrix and continued in further development and improvement of machine translation (MT) systems for languages of the European Union (EU).
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani, the founders of the software company Explosion.
A confusion network is a natural language processing method that combines outputs from multiple automatic speech recognition or machine translation systems. Confusion networks are simple linear directed acyclic graphs with the property that each a path from the start node to the end node goes through all the other nodes. The set of words represented by edges between two nodes is called a confusion set. In machine translation, the defining characteristic of confusion networks is that they allow multiple ambiguous inputs, deferring committal translation decisions until later stages of processing. This approach is used in the open source machine translation software Moses and the proprietary translation API in IBM Bluemix Watson.
