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  1. The Pros and Cons of Statistical Machine Translation › blog › pros-and-cons

    SMT uses statistical analysis and predictive algorithms to define rules that are best suited for target sentence translation. These models are trained using a bilingual corpus. Based on the subject matter text that is used to train a corpus, the SMT will be best suited for documents pertaining to the same subject.

  2. What is Machine Translation? Rule Based vs. Statistical | SYSTRAN › what-is-machine-translation

    Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora.

  3. People also ask

    What is statistical machine translation?

    What kind of Technology is used for text translation?

    How is machine translation used in human language?

    What is the purpose of machine translation ( NMT )?

  4. What is Machine Translation? - KantanMT › documents › Machine_Translation

    MACHINE TRANSLATION Statistical Machine Translation (SMT) Technology Statistical Machine Translation utilizes statistical translation models generated from the analysis of monolingual and bilingual training data. Essentially, this approach uses computing power to build sophisticated data models to translate one source language into another.

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  5. Machine translation - Wikipedia › wiki › Machine_translation

    Statistical machine translation tries to generate translations using statistical methods based on bilingual text corpora, such as the Canadian Hansard corpus, the English-French record of the Canadian parliament and EUROPARL, the record of the European Parliament.

  6. Different Types Of Machine Translation - Localize Blog › articles › types-of-machine-translation

    Jun 22, 2018 · Statistical Machine Translation (SMT) SMT works by referring to statistical models that are based on the analysis of large volumes of bilingual text. It aims to determine the correspondence between a word from the source language and a word from the target language. A good example of this is Google Translate.

  7. Machine Translation and the Evaluation of Its Quality ... › books › recent-trends-in

    May 16, 2019 · Statistical machine translation was a dominant approach over the past 20 years. It brought many cases of practical use. It is described in more detail in this chapter. Statistical machine translation is not equally successful for all language pairs.

    • Mirjam Sepesy Maučec, Gregor Donaj
    • 2
    • 2019
  8. A Gentle Introduction to Neural Machine Translation › introduction-neural
    • What Is Machine Translation?
    • What Is Statistical Machine Translation?
    • What Is Neural Machine Translation?
    • Summary

    Machine translation is the task of automatically converting source text in one language to text in another language.— Page 98, Deep Learning, 2016.Given a sequence of text in a source language, there is no one single best translation of that text to another language. This is because of the natural ambiguity and flexibility of human language. This makes the challenge of automatic machine translation difficult, perhaps one of the most difficult in artificial intelligence:— Page 21, Artificial I...

    Statistical machine translation, or SMT for short, is the use of statistical models that learn to translate text from a source language to a target language gives a large corpus of examples.This task of using a statistical model can be stated formally as follows:— A Statistical Approach to Machine Translation, 1990.This formal specification makes the maximizing of the probability of the output sequence given the input sequence of text explicit. It also makes the notion of there being a suite...

    Neural machine translation, or NMT for short, is the use of neural network models to learn a statistical model for machine translation.The key benefit to the approach is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning.— Neural Machine Translation by Jointly Learning to Align and Translate, 2014.As such, neural machine translation systems are said to be end-to-end systems as onl...

    In this post, you discovered the challenge of machine translation and the effectiveness of neural machine translation models.Specifically, you learned: 1. Machine translation is challenging given the inherent ambiguity and flexibility of human language. 2. Statistical machine translation replaces classical rule-based systems with models that learn to translate from examples. 3. Neural machine translation models fit a single model rather than a pipeline of fine tuned models and currently achie...

  9. Machine Translation - Microsoft Translator for Business › business › machine-translation
    • Summary
    • Technology
    • Early history
    • Impact
    • Availability
    • Uses
    • Operation
    • Benefits
    • Advantages
    • Performance
    • Example
    • Details
    • Software

    Machine translation systems are applications or online services that use machine-learning technologies to translate large amounts of text from and to any of their supported languages. The service translates a source text from one language to a different target language.

    Although the concepts behind machine translation technology and the interfaces to use it are relatively simple, the science and technologies behind it are extremely complex and bring together several leading-edge technologies, in particular, deep learning (artificial intelligence), big data, linguistics, cloud computing, and web APIs. Since the early 2010s, a new artificial intelligence technology, deep neural networks (aka deep learning), has allowed the technology of speech recognition to reach a quality level that allowed the Microsoft Translator team to combine speech recognition with its core text translation technology to launch a new speech translation technology. Both SMT and NMT translation technologies have two elements in common: There are two main technologies used for text translation: the legacy one, Statistical Machine Translation (SMT), and the newer generation one, Neural Machine Translation (NMT).

    Historically, the primary machine learning technique used in the industry was Statistical Machine Translation (SMT). SMT uses advanced statistical analysis to estimate the best possible translations for a word given the context of a few words. SMT has been used since the mid-2000s by all major translation service providers, including Microsoft.

    The advent of Neural Machine Translation (NMT) caused a radical shift in translation technology, resulting in much higher quality translations. This translation technology started deploying for users and developers in the latter part of 2016.

    Microsoft Translator text API has been used by Microsoft groups since 2007 and is available as an API for customers since 2011. The Microsoft Translator text API is used extensively within Microsoft. It is incorporated across product localization, support, and online communication teams (e.g., Windows blog). This same service is also accessible, at no additional cost, from within familiar Microsoft products such as Bing, Cortana, Microsoft Edge, Office, SharePoint, Skype, and Yammer. Speech translation is now available through Microsoft Speech, an end-to-end set of fully customizable services for speech recognition, speech translation, and speech synthesis (text-to-speech).

    Microsoft Translator can be used in web or client applications on any hardware platform and with any operating system to perform language translation and other language-related operations such as language detection, text to speech, or dictionary. Based on the neural-network training, each word is coded along a 500-dimensions vector (a) representing its unique characteristics within a particular language pair (e.g. English and Chinese). Based on the language pairs used for training, the neural network will self-define what these dimensions should be. They could encode simple concepts like gender (feminine, masculine, neutral), politeness level (slang, casual, written, formal, etc.), type of word (verb, noun, etc.), but also any other non-obvious characteristics as derived from the training data.

    Leveraging industry standard REST technology, the developer sends source text (or audio for speech translation) to the service with a parameter indicating the target language, and the service sends back the translated text for the client or web app to use.

    The Microsoft Translator service is an Azure service hosted in Microsoft data centers and benefits from the security, scalability, reliability, and nonstop availability that other Microsoft cloud services also receive.

    Because of this approach, which does not rely on dictionaries or grammatical rules, it provides the best translations of phrases where it can use the context around a given word versus trying to perform single word translations. For single words translations, the bilingual dictionary was developed and is accessible through

    Neural network translations fundamentally differ in how they are performed compared to the traditional SMT ones.

    The following animation depicts the various steps neural network translations go through to translate a sentence. Because of this approach, the translation will take into context the full sentence, versus only a few words sliding window that SMT technology uses and will produce more fluid and human-translated looking translations. In the example depicted in the animation, the context-aware 1000-dimension model of the will encode that the noun (house) is a feminine word in French (la maison). This will allow the appropriate translation for the to be la and not le (singular, male) or les (plural) once it reaches the decoder (translation) layer.

    The attention algorithm will also calculate, based on the word(s) previously translated (in this case the), that the next word to be translated should be the subject (house) and not an adjective (blue). In can achieve this because the system learned that English and French invert the order of these words in sentences. It would have also calculated that if the adjective were to be big instead of a color, that it should not invert them (the big house => la grande maison).

    Translations using the speech translation API (as a developer) or in a speech translation app or service, is powered with the newest neural-network based translations for all the speech-input supported languages (see here for the full list). These models were also built by expanding the current, mostly written-text trained translation models, with more spoken-text corpora to build a better model for spoken conversation types of translations. These models are also available through the speech standard category of the traditional text translation API.

  10. Machine Translation in 2020 | Smartling › state-of-machine-translation
    • What Is Machine Translation?
    • The History of Machine Translation
    • Machine Translation in 2020
    • Adaptive Machine Translation vs Out of The Box Machine Translation
    • When to Use Machine Translation?
    • Machine Translation and Human Review
    • Machine Translation in Smartling
    • Smartling Lets You Decide
    • Where Will Machine Translation Go from Here?

    Machine translation (MT) is the set of tools that enable users to input text in one language, and the engine will generate a complete translation in a new target language. MT has been around for a bit longer than some may realize and the technology has come a long way from its origins. Modern machine learning and neural networks have pushed MT into an entirely new realm. With usable and modern MT available, the question shouldn’t be if you decide to use MT, but rather, “when is the right time to use MT?”

    The short story is this: machine translation stumbled first to become a usable solution, and was almost abandoned outright. Yet, modern tech break-throughs have awakened machine translation, specifically leveraging powerful neural networks for machine learning. These are the four most prominent milestonesof machine translations: 1. 1949: MT started its infancy conceptually, with a physical device and public presentation finally making an appearance in 1954, by a Georgetown MT research team. 2. 1966: The National Academy of Science formed a specific MT committee, known as ALPAC. A report published by ALPAC just a few years later almost decimated the industry, with strong suggestions to stop funding and for research to come to a halt -- the technology simply wasn't there to make it a possibility. 3. 1997: MT makes its way into the mainstream as the internet takes off: AltaVista’s well-known Babelfish was introduced in '97, and Google Translate came to be almost 10 years later in '06....

    For a quick high-level view, and to provide further context of how MT will fit into your business' strategy, let's take a closer look at the two major distinctions in modern MT -- Statistical and neural Network based engines. 1. Statistic Based engineslearn through statistical analysis of a bilingual text, generally provided by the developer or user. These engines essentially develop an understanding of existing rules to determine the unique relationship between the source language and target language. 2. Neural Based enginesare the most modern approach to MT. Neural networks are designed to mimic how the human mind learns, gaining more knowledge over time. These engines seek to understand the context of what is being translated to properly predict the correct word choice. Neural based machine translation engines are much more capable of capturing, and even understanding, the intent or meaning of a sentence, and therefore have been quickly replacing older statistical models. The ide...

    Part of the beauty behind modern neural Machine Translation engines is the ability for not only rapid deployment but almost complete customization. That's part of what makes them the best machine translation models we have so far. When leveraged as a service, most neural Machine Translation engines can either be used straight out of the box -- but require in-depth training -- or can be pre-adapted for one specific brand or domain. Training these engines alone can be a time-consuming process, but working with the right vendor can ensure not only quicker deployment, but also much more accurate results -- that is why Smartling offers integrations with the pioneers behind both neural networks and MT. An adaptive NMT engine will constantly be growing its knowledge of your brand’s content, voice, tone and overall style to deliver localized content that still captures the exact presentation and quality experience unique to your brand.

    Machine Translation is impressive technology and has come far from those early stages. Modern engines are now capable of competing with human translators, to a degree. In fact, we have a whole webinar dedicated explaining how content managers can incorporate MT into their translation process. But in summary, when discussing MT we should be thinking along the lines of: "when do I hand off content to a human translator, and when do I let MT handle the task?" The biggest factor will come down to content priority. Some complexities to consider might be: 1. Where is this content going? 2. What is the target audience? 3. What demand is this content fulfilling? 4. Can content be edited or revised after publication? 5. Are there any legal restrictions or strict brand guidelines surrounding this content? High priority critical content, for example legal documentation or medical information, will require the expertise of a human translator. Due to the nature of this content, and the high prio...

    Machine Translation enables businesses to edge even closer to that perfect balance of moving content at a rapid pace and pushing to market as quickly as possible while reducing the time and capital spent on projects. But, just because a project might fit into this criteria does not mean it will be ripe for MT -- and it doesn't mean that a human will never be involved in the process. Whether or not the translation process will require internal reviewfor every project is something that must be determined. In our experience, while content spends the most time during this process, only 4% of translations receive edits. So the question then becomes a determination of which path makes more sense: 1. Should we rely on MT do the majority of the work and build in an internal check process with a human translator? 2. Should we simply let a human handle the translation with an automated quality check working to ensure consistency throughout the process? For example, translating user reviews of...

    We recognize that not every job will require a human translator. That's why Smartling makes it easyfor customers to find the path that works best for each project. Smartling already connects with the best Machine Translation engines: 1. Amazon Translate 2. CrossLang 3. DeepL 4. Google Translate 5. Microsoft Translator 6. Unbabel 7. Watson Language Translator More recent innovations in this space is the advent of Neural Machine Translation. Neural machine translation (NMT) is designed to learn language much like the human brain does, adapting to your brand’s unique voice and tone overtime. With direct integrations to leading providers, Smartling positions you to integrate with the best machine translation services possible. But to understand how NMT has become so powerful, let's look a little bit further at exactly where MT came from, and how far it has evolved.

    Leveraging the best machine translation is more about balancing the content to move low priority, high volume content as fast as possible. This should, therefore, enable the human translators to focus on more important and crucial content. With Smartling users can configure a Translation Workflow step to automatically assign content to the proper translation resource, whether that be a human translator or MT engine. Content can even be rejected, or assigned to a professional translator based on the complexity of the project. For example, Lucidchart began leveraging Smartlingto drastically improve their translation speed and agility, with the goal of rapid deployment to match their fast release cycles. Previous translation methods couldn’t keep up and were slowing down the entire roadmap. By injecting NMT into the process, Lucidchart can provide a consistent and cohesive localized product experience to their international users, without any negative impact on their product and releas...

    In summary, machine translation has made huge strides since the very early days. Even 10 years back, with Google Translate pushing the concept mainstream, MT was far from perfect. Modern solutions leveraging powerful machine learning are constantly pushing new boundaries and will only continue to improve overtime as their knowledge grows. However, there will always be a need for a personal touch at some point, and automation will not entirely replace human translators. Even the best machine translation engine will struggle with highly creative and complex content. When it comes to localization, you’re going to be faced with this question: should I use machine translation? Human translators are the ones that will provide that extra level of creativity for the highest quality of work. Machine translation is all about getting the job done with a margin for error. Want to chat with an expert? Give us a call, Smartling is here to help you move the world with words.

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