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Solving this problem with corpus statistical, and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.\r\nCurrent machine translation software often allows for customization by domain or profession (such as weather reports), improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text.\r\nImproved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are proper names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).\r\nThe progress and potential of machine translation have been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality, first and most notably by Yehoshua Bar-Hillel. Some critics claim that there are in-principle obstacles to automating the translation process.","materialsDescription":" <span style=\"font-weight: bold; \">What is Machine Translation?</span>\r\nMachine translation (MT) is an automated translation by computer software. MT can be used to translate entire texts without any human input or can be used alongside human translators. The concept of MT started gaining traction in the early 50s and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.\r\n<span style=\"font-weight: bold; \">How Does Machine Translation Work?</span>\r\nWell, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.\r\nRule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.\r\nStatistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource-intensive. When using domain-specific data, SMT can produce good quality translations, especially in the technical, medical, and financial fields.\r\nNeural MT (NMT) is a new approach that is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.\r\n<span style=\"font-weight: bold; \">What are the Pros and Cons of Machine Translation?</span>\r\n<ul><li>MT is incredibly fast and can translate thousands of words per minute.</li><li>It can translate into multiple languages at once which drastically reduces the amount of manpower needed.</li><li>Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.</li><li>MT technology is developing rapidly and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.</li></ul>\r\nThere are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.\r\n<span style=\"font-weight: bold; \">When Should You Use Machine Translation?</span>\r\nWhen translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.\r\nFor large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.\r\nUsing a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.\r\n<span style=\"font-weight: bold;\">Which MT Engine Should You Use?</span>\r\nNot all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content type.\r\nUltimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.\r\n<span style=\"font-weight: bold;\">Using Machine Translation and a Translation Management System</span>\r\nYou can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS.&nbsp; With these technologies integrated, you will be able to leverage additional tools such as translation memories, termbases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Machine_Translation.png"},{"id":425,"title":"Linguistics Software","alias":"linguistics-software","description":" <span style=\"font-weight: bold; \">Linguistic software</span> - computer programs and data that provide analysis, processing, storage and retrieval of audio data, pictures (OCR) and texts in natural language.<span style=\"font-weight: bold; \"></span>\r\n<span style=\"font-weight: bold; \">Linguistic analysis software</span> is a tool that enhances the comprehension of information present in documents, or across a set of documents. Some linguistic analysis software tools use an automatic mathematical or statistical approach to analyze and extract relations that occur in a set of documents and the words they contain. The idea is that the distribution and frequency of relevant terms in documents that cover a specific topic is similar and that the meaning of a specific word can be inferred by the above assumption. \r\nThe term CAT stands for <span style=\"font-weight: bold;\">Computer-Assisted Translation Tool</span>. CAT tools have significantly changed the way how translators work and manage translation projects today. CAT tools split large multilingual documents into segments (phrases &amp; paragraphs) which are stored in a database. This is called translation memory which means that previously translated material can be reused at any time. Nowadays, it is more common for enterprises and translators to use CAT tools to speed up their work and increase their productivity globally.A number of computer-assisted translation software and websites exists for various platforms and access types.\r\nAccording to a 2006 survey undertaken by Imperial College of 874 translation professionals from 54 countries, primary tool usage was reported as follows: Trados (35%), Wordfast (17%), Déjà Vu (16%), SDL Trados 2006 (15%), SDLX (4%), STAR Transit (3%), OmegaT (3%), others (7%).\r\n\r\n<br /><br />","materialsDescription":"<h1 class=\"align-center\">CAT Tool Functionality</h1>\r\n<ul><li>Spell checkers, autocorrect—automatically highlight and fix spelling and grammar mistakes.</li><li>In-context review—display multimedia documents with images, text box sizes and layout in real time in both source and target language.</li><li>Integrated machine translation—suggest translations for segments from a connected MT engine.</li><li>Adaptive machine translation—offer translations for segments from a connected MT engine as autosuggest pop-ups, learn from user input.</li><li>Concordance—retrieve instances of a word or an expression and their respective context in a text corpus, such as a translation memory database, to check their usage.</li><li>Electronic dictionaries—allow term search inside the tool, track usage statistics.</li><li>Text search tools—find phrases or terms in the text for reference.</li><li>Alignment—build translation memories texts from source text and its translation. When a translation company adopts a CAT-tool, they often use alignment to create their first TM databases.</li></ul>\r\nThere are online and offline CAT tools available for purchase. Web-based CAT tools work in the web browser; desktop CAT tools require installation and do not depend on internet connection stability.\r\nBasic CAT tools support MS Office formats, such as .docx, .odt, .csv, .xlsx, plus .html files and .xml. The primary translation industry formats are XLIFF, and tmx. Advanced tools also support various software formats, such as .json, .properties, Visual Studio, as well layout building software such as InDesign, Corel Draw and sometimes AutoCAD.\r\nCAT tools are useful when translating multimedia formats. They extract text for editing and rebuild files in the target language after the translation is finished. This ability reduces the time needed to create multilingual artwork. Instead of fishing for each individual bit of text on the page, the translator simply goes segment by segment in plain text.\r\n<p class=\"align-center\"><span style=\"font-weight: bold;\">Automated Quality Assurance (AutoQA)</span></p>\r\nAutoQA tools scan bilingual texts and detect errors in translation, such as wrong numbers and number format, incorrect terminology, missing tags, missing segments, erroneous formatting and many others. Complementary to spellcheckers, AutoQA helps editing.\r\nWhile popular translation memory tools feature built-in QA components, standalone software offers extended functionality and support for morphology of selected languages. Advanced functionality leads to better error detection and fewer false positives. 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Such systems usually include guidance on terms that should not be translated to target languages or additional reference material and images to assist translators in understanding the translation material.\r\nAdvanced terminology systems include integrations with authoring tools, workflows for terminology creation and validation processes, and automated term mining functions.<br /><br />","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Linguistics_Software.png"}],"characteristics":[],"concurentProducts":[],"jobRoles":[],"organizationalFeatures":[],"complementaryCategories":[],"solutions":[],"materials":[],"useCases":[],"best_practices":[],"values":[],"implementations":[]},"promt-translation-server":{"id":255,"logoURL":"https://old.roi4cio.com/fileadmin/user_upload/PROMT_Translation_Server1.jpg","logo":true,"scheme":false,"title":"PROMT Translation Server","vendorVerified":0,"rating":"2.50","implementationsCount":2,"suppliersCount":0,"supplierPartnersCount":1,"alias":"promt-translation-server","companyTitle":"PROMT","companyTypes":["supplier","vendor"],"companyId":2086,"companyAlias":"promt","description":"Efficient and safe work with documents in any foreign language Eliminating language barriers in-house and beyond Speeding up business processes related to foreign documentation Improving personal productivity of employees while working with any multilingual content Who uses the solution? Every employee of the company can use this solution: Heads – to translate business correspondence fast and to read legal and financial documents. Managers – to work with commercial offers and agreements and to translate documentation for export or import products. Lawyers – to study foreign legislation and to translate agreements. IT Specialists – to translate technical texts, to write user guides, and to translate descriptions of equipment and system messages. Engineers – to work with documentation for import equipment, standards and regulatory norms, and to read professional literature. Developers – to read user guides, scientific articles, and patent documentation. Marketing Specialists – to study foreign markets, to read world news, and to work with analytical materials. HR Managers – to review resumes and to read business correspondence. 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Solving this problem with corpus statistical, and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.\r\nCurrent machine translation software often allows for customization by domain or profession (such as weather reports), improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text.\r\nImproved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are proper names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).\r\nThe progress and potential of machine translation have been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality, first and most notably by Yehoshua Bar-Hillel. Some critics claim that there are in-principle obstacles to automating the translation process.","materialsDescription":" <span style=\"font-weight: bold; \">What is Machine Translation?</span>\r\nMachine translation (MT) is an automated translation by computer software. MT can be used to translate entire texts without any human input or can be used alongside human translators. The concept of MT started gaining traction in the early 50s and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.\r\n<span style=\"font-weight: bold; \">How Does Machine Translation Work?</span>\r\nWell, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.\r\nRule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.\r\nStatistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource-intensive. When using domain-specific data, SMT can produce good quality translations, especially in the technical, medical, and financial fields.\r\nNeural MT (NMT) is a new approach that is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.\r\n<span style=\"font-weight: bold; \">What are the Pros and Cons of Machine Translation?</span>\r\n<ul><li>MT is incredibly fast and can translate thousands of words per minute.</li><li>It can translate into multiple languages at once which drastically reduces the amount of manpower needed.</li><li>Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.</li><li>MT technology is developing rapidly and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.</li></ul>\r\nThere are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.\r\n<span style=\"font-weight: bold; \">When Should You Use Machine Translation?</span>\r\nWhen translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.\r\nFor large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.\r\nUsing a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.\r\n<span style=\"font-weight: bold;\">Which MT Engine Should You Use?</span>\r\nNot all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content type.\r\nUltimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.\r\n<span style=\"font-weight: bold;\">Using Machine Translation and a Translation Management System</span>\r\nYou can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS.&nbsp; With these technologies integrated, you will be able to leverage additional tools such as translation memories, termbases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Machine_Translation.png"},{"id":425,"title":"Linguistics Software","alias":"linguistics-software","description":" <span style=\"font-weight: bold; \">Linguistic software</span> - computer programs and data that provide analysis, processing, storage and retrieval of audio data, pictures (OCR) and texts in natural language.<span style=\"font-weight: bold; \"></span>\r\n<span style=\"font-weight: bold; \">Linguistic analysis software</span> is a tool that enhances the comprehension of information present in documents, or across a set of documents. Some linguistic analysis software tools use an automatic mathematical or statistical approach to analyze and extract relations that occur in a set of documents and the words they contain. The idea is that the distribution and frequency of relevant terms in documents that cover a specific topic is similar and that the meaning of a specific word can be inferred by the above assumption. \r\nThe term CAT stands for <span style=\"font-weight: bold;\">Computer-Assisted Translation Tool</span>. CAT tools have significantly changed the way how translators work and manage translation projects today. CAT tools split large multilingual documents into segments (phrases &amp; paragraphs) which are stored in a database. This is called translation memory which means that previously translated material can be reused at any time. Nowadays, it is more common for enterprises and translators to use CAT tools to speed up their work and increase their productivity globally.A number of computer-assisted translation software and websites exists for various platforms and access types.\r\nAccording to a 2006 survey undertaken by Imperial College of 874 translation professionals from 54 countries, primary tool usage was reported as follows: Trados (35%), Wordfast (17%), Déjà Vu (16%), SDL Trados 2006 (15%), SDLX (4%), STAR Transit (3%), OmegaT (3%), others (7%).\r\n\r\n<br /><br />","materialsDescription":"<h1 class=\"align-center\">CAT Tool Functionality</h1>\r\n<ul><li>Spell checkers, autocorrect—automatically highlight and fix spelling and grammar mistakes.</li><li>In-context review—display multimedia documents with images, text box sizes and layout in real time in both source and target language.</li><li>Integrated machine translation—suggest translations for segments from a connected MT engine.</li><li>Adaptive machine translation—offer translations for segments from a connected MT engine as autosuggest pop-ups, learn from user input.</li><li>Concordance—retrieve instances of a word or an expression and their respective context in a text corpus, such as a translation memory database, to check their usage.</li><li>Electronic dictionaries—allow term search inside the tool, track usage statistics.</li><li>Text search tools—find phrases or terms in the text for reference.</li><li>Alignment—build translation memories texts from source text and its translation. When a translation company adopts a CAT-tool, they often use alignment to create their first TM databases.</li></ul>\r\nThere are online and offline CAT tools available for purchase. Web-based CAT tools work in the web browser; desktop CAT tools require installation and do not depend on internet connection stability.\r\nBasic CAT tools support MS Office formats, such as .docx, .odt, .csv, .xlsx, plus .html files and .xml. The primary translation industry formats are XLIFF, and tmx. Advanced tools also support various software formats, such as .json, .properties, Visual Studio, as well layout building software such as InDesign, Corel Draw and sometimes AutoCAD.\r\nCAT tools are useful when translating multimedia formats. They extract text for editing and rebuild files in the target language after the translation is finished. This ability reduces the time needed to create multilingual artwork. Instead of fishing for each individual bit of text on the page, the translator simply goes segment by segment in plain text.\r\n<p class=\"align-center\"><span style=\"font-weight: bold;\">Automated Quality Assurance (AutoQA)</span></p>\r\nAutoQA tools scan bilingual texts and detect errors in translation, such as wrong numbers and number format, incorrect terminology, missing tags, missing segments, erroneous formatting and many others. Complementary to spellcheckers, AutoQA helps editing.\r\nWhile popular translation memory tools feature built-in QA components, standalone software offers extended functionality and support for morphology of selected languages. Advanced functionality leads to better error detection and fewer false positives. 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Such systems usually include guidance on terms that should not be translated to target languages or additional reference material and images to assist translators in understanding the translation material.\r\nAdvanced terminology systems include integrations with authoring tools, workflows for terminology creation and validation processes, and automated term mining functions.<br /><br />","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Linguistics_Software.png"}],"characteristics":[],"concurentProducts":[],"jobRoles":[],"organizationalFeatures":[],"complementaryCategories":[],"solutions":[],"materials":[],"useCases":[],"best_practices":[],"values":[],"implementations":[]}},"aliases":{"1":["promt-promt","promt-translation-server"]},"links":{"first":"http://b4r_be/api/products?page=1","last":"http://b4r_be/api/products?page=1","prev":null,"next":null},"meta":{"current_page":1,"from":1,"last_page":1,"path":"http://b4r_be/api/products","per_page":20,"to":2,"total":2},"loading":false,"error":null,"useProductLoading":false,"sellProductLoading":false,"templatesById":{},"comparisonByTemplateId":{}},"filters":{"filterCriterias":{"loading":false,"error":null,"data":{"price":{"min":0,"max":6000},"users":{"loading":false,"error":null,"ids":[],"values":{}},"suppliers":{"loading":false,"error":null,"ids":[],"values":{}},"vendors":{"loading":false,"error":null,"ids":[],"values":{}},"roles":{"id":200,"title":"Roles","values":{"1":{"id":1,"title":"User","translationKey":"user"},"2":{"id":2,"title":"Supplier","translationKey":"supplier"},"3":{"id":3,"title":"Vendor","translationKey":"vendor"}}},"categories":{"flat":[],"tree":[]},"countries":{"loading":false,"error":null,"ids":[],"values":{}}}},"showAIFilter":false},"companies":{"companiesByAlias":{},"aliases":{},"links":{},"meta":{},"loading":false,"error":null},"implementations":{"implementationsByAlias":{},"aliases":{},"links":{},"meta":{},"loading":false,"error":null},"agreements":{"agreementById":{},"ids":{},"links":{},"meta":{},"loading":false,"error":null},"comparison":{"loading":false,"error":false,"templatesById":{},"comparisonByTemplateId":{},"products":[],"selectedTemplateId":null},"presentation":{"type":null,"company":{},"products":[],"partners":[],"formData":{},"dataLoading":false,"dataError":false,"loading":false,"error":false},"catalogsGlobal":{"subMenuItemTitle":""}}