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  1. Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language.[1]
  2. Classical machine translation methods often involve rules for converting text in the source language to the target language.[1]
  3. Although effective, statistical machine translation methods suffered from a narrow focus on the phrases being translated, losing the broader nature of the target text.[1]
  4. Although effective, the neural machine translation systems still suffer some issues, such as scaling to larger vocabularies of words and the slow speed of training the models.[1]
  5. If you order MT from us, the translation takes place in a closed system via encrypted transfer.[2]
  6. Not sure if machine translation is what you need?[2]
  7. Up until late 2016, all of the machine translation products on the market were based on algorithms that use statistical methods to try to ascertain the best possible translation for a given word.[2]
  8. This technology is known as Statistical Machine Translation (SMT).[2]
  9. Machine translation (MT) is automated translation.[3]
  10. Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora.[3]
  11. Rule-based MT provides good out-of-domain quality and is by nature predictable.[3]
  12. Statistical MT provides good quality when large and qualified corpora are available.[3]
  13. Audit files are automatically generated by SDL Trados Studio which record the use of machine translation.[4]
  14. The progress and potential of machine translation have been much debated through its history.[5]
  15. The idea of machine translation later appeared in the 17th century.[5]
  16. The memorandum written by Warren Weaver in 1949 is perhaps the single most influential publication in the earliest days of machine translation.[5]
  17. A Georgetown University MT research team, led by Professor Michael Zarechnak, followed (1951) with a public demonstration of its Georgetown-IBM experiment system in 1954.[5]
  18. 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.[6]
  19. Historically, the primary machine learning technique used in the industry was Statistical Machine Translation (SMT).[6]
  20. The advent of Neural Machine Translation (NMT) caused a radical shift in translation technology, resulting in much higher quality translations.[6]
  21. If you’re involved with translation or localization, you already know what machine translation is.[7]
  22. There are four types of machine translation– Statistical Machine Translation (SMT), Rule-based Machine Translation (RBMT), Hybrid Machine Translation, and Neural Machine Translation.[7]
  23. Integrating machine translation into the localization strategy is a must now.[7]
  24. Speaking of choice, which type of machine translation should you opt for?[7]
  25. Machine Translation is an excellent example of how cutting-edge research and world-class infrastructure come together at Google.[8]
  26. Exciting research challenges abound as we pursue human quality translation and develop machine translation systems for new languages.[8]
  27. Neural Machine Translation describes an approach in which a large neural network is trained using machine learning techniques.[9]
  28. For example, Google developed its own artificial intelligence programs and applied them to its own multilingual neural machine translation system.[9]
  29. However, while the quality of machine translation has improved drastically over the years, humans can still add a lot of value.[9]
  30. Google Translate is the most widely known and most used machine translation technology used globally.[10]
  31. Natural language machine translation is well on its way to becoming the single most important productivity enhancement technology for human translators.[10]
  32. In that sense, the pure play machine translation technology market relates to the development and deployment of machine translation technology without any human translation services.[10]
  33. The history of machine translation is divided into three main eras: Rules-based (1960s), Statistical (ca. 2007) and Neural (2016 onward).[10]
  34. WMT shared task on news translation provides a new test-set (with ~3000 sentences) each year collected from recent news articles (WMT = Workshop on statistical Machine Translation.[11]
  35. In 2016, WMT was renamed to Conference on Machine Translation, but keeping the legacy abbreviation WMT.[11]
  36. Toral et al.22 furthermore warned about post-edited MT used as human references.[11]
  37. BLEU28 is a popular automatic measure for MT evaluation and we use it for hyperparameter tuning.[11]
  38. While there have been many variants, most MT systems, and certainly those that have found practical application, have parts that can be named for the chapters in a linguistic text book.[12]
  39. It’s hard to imagine our globalized world without machine translation engines.[13]
  40. In this article, we will cover some of the most well-known machine translation engines.[13]
  41. Probably the most used machine translation service, Google Translate covers 103 languages.[13]
  42. Google Translate started as a statistical machine translation service in 2006.[13]
  43. Wondering how to improve machine translation quality?[14]
  44. This is because in 2020, the improvement of machine translation output depends on human involvement from the user by editing translations and training your machine translation engine.[14]
  45. So if you want to know how to improve machine translation quality, there are two ways to do so.[14]
  46. Below, we’ll go into more detail about these two methods for how to improve machine translation quality.[14]
  47. Neural machine translation (NMT) is typically software used to translate words from one language to another.[15]
  48. As you can see in the chart above, neural machine translation technology is currently state-of-the-art technology in machine translation and offers the highest quality translation.[15]
  49. Technically, NMTs encompass all types of machine translation where an artificial neural network is used to predict a sequence of numbers when provided with a sequence of numbers.[15]
  50. In fact, we believe humans will always play a critical step in machine translation to review the accuracy and context of machine.[15]
  51. In machine translation, texts are translated automatically with computer software so that a human translator does not directly participate in the process.[16]
  52. The relationship between human translators and machine translation systems has been that the machine translation engine has been trained with translations created by human translators.[16]
  53. Machine translation engines can produce the same quality as a human translator for a few text types only.[16]
  54. Today, machine translation works best in scenarios where a text needs to be conveyed in an understandable form in another language.[16]
  55. The basic analogical machine translation architecture, involving matching, recombination and generation is also presented.[17]
  56. For machine translation the languages will determine which senses are relevant.[17]
  57. Nevertheless, machine translation can be quite impressive in the right circumstances.[17]
  58. For example, experiments in statistical named-entity detection or machine translation may benefit from using a deep parser as a feature generator.[17]
  59. Even though we’re nearing the end of 2017, machine translation tools are still not advanced enough to replace the humans.[18]
  60. When it comes to machine translation there is still a lot of confusion, particularly for the people coming from the other industry fields.[18]
  61. Machine translation (MT) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.[18]
  62. This type of machine translation requires more information about the structure of the source and target languages.[18]
  63. Machine translation is continuously improving, but it can be difficult to know which machine translation engine is best for your content.[19]
  64. Machine Translation Quality Estimation (MTQE) is included for free, to help guide your post-editing process.[19]
  65. Machine translation engines integrated with Crowdin provide translation suggestions from the automatic translation services like Google Translate and AutoML Translation, Microsoft Translate, Yandex.[20]
  66. MT has been around for a bit longer than some may realize and the technology has come a long way from its origins.[21]
  67. The short story is this: machine translation stumbled first to become a usable solution, and was almost abandoned outright.[21]
  68. : MT started its infancy conceptually, with a physical device and public presentation finally making an appearance in 1954, by a Georgetown MT research team.[21]
  69. The National Academy of Science formed a specific MT committee, known as ALPAC.[21]
  70. Implement an encoder-decoder model with attention which you can read about in the TensorFlow Neural Machine Translation (seq2seq) tutorial.[22]
  71. The vain struggles to improve machine translation lasted for forty years.[23]
  72. In 1966, the US ALPAC committee, in its famous report, called machine translation expensive, inaccurate, and unpromising.[23]
  73. The first ideas surrounding rule-based machine translation appeared in the 70s.[23]
  74. Japan was especially interested in fighting for machine translation.[23]
  75. Over the years, multiple organizations have cropped up with intelligent services and products offering domain-specific machine translation.[24]
  76. A machine translation system can be adapted to a specific domain by using training data from the same domain.[24]
  77. SYSTRAN products run on the company’s own neural network engine, which it calls as Pure Neural Machine Translation.[24]
  78. Facebook has been experimenting with machine translation for close to a decade.[24]
  79. To understand the disruptive force of neural machine translation, it is useful to retrace its genealogy within the history of translation.[25]
  80. Since the advent of computers and digital text processing, scientists have tried to devise automated machine translation tools.[25]
  81. Rule-based machine translation processing was lengthy and error-ridden because it could not take into account the ambiguities and quirks of real-life language.[25]
  82. Then toward the end of the 80s, researchers, notably from IBM and the German branch of Systran, developed statistical machine translation (SMT).[25]
  83. The progress in machine translation (MT) has reached many remarkable milestones over the last few years, and it is likely that it will progress further.[26]
  84. Building an MT system relies on the availability of parallel data.[26]
  85. In scientific literature for machine translation, there is no particular consensus on which corpus size constitutes a low-resource scenario.[26]
  86. But we can say roughly that a low-resource condition is when the size of the parallel training corpus is not sufficient for reaching an acceptable result with the standard MT approaches.[26]
  87. The kind of user generated content that Reddit and similar websites aggregate often contains orthographic variations that are particularly tough for regular MT systems.[27]
  88. Any if the above will typically cause problems for any standard MT system.[27]
  89. We observed that MT models trained on given data were particularly brittle and often generated hallucinations (output completely unrelated to input) or copies (exact copy of the source).[27]
  90. Back-translation ‘BT’: we use MT models in reverse direction to translate target-language monolingual data (the small MTNT corpus and the huge news-discuss corpus) to the source language.[27]
  91. Machine translation has already become part of our everyday life.[28]
  92. This chapter gives an overview of machine translation approaches.[28]
  93. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future.[28]
  94. This chapter also describes the evaluation of machine translation quality.[28]
  95. It covers the three main approaches of machine translation as well as several challenges of the field.[29]
  96. Machine translation (MT) is the task to translate a text from a source language to its counterpart in a target language.[29]
  97. The neural approach uses neural networks to achieve machine translation.[29]
  98. In this story, we covered the three approaches to the problem of Machine Translation.[29]
  99. Machine Translation (MT) has advanced significantly over the last few years.[30]
  100. Phrase has accepted this new role of MT and offers it as a natural part of its services to all customers.[30]
  101. Once this is enabled you can already use machine translation for your translations.[30]
  102. Alternatively, you can define different machine translation providers for different language pairs.[30]

소스

  1. 1.0 1.1 1.2 1.3 A Gentle Introduction to Neural Machine Translation
  2. 2.0 2.1 2.2 2.3 Machine translation
  3. 3.0 3.1 3.2 3.3 What is Machine Translation? Rule Based vs. Statistical
  4. What is Machine Translation?
  5. 5.0 5.1 5.2 5.3 Machine translation
  6. 6.0 6.1 6.2 Microsoft Translator for Business
  7. 7.0 7.1 7.2 7.3 Different Types Of Machine Translation
  8. 8.0 8.1 Machine Translation – Google Research
  9. 9.0 9.1 9.2 Machine Translation
  10. 10.0 10.1 10.2 10.3 Latest Machine Translation Technology Analysis & News in 2020
  11. 11.0 11.1 11.2 11.3 Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals
  12. Linguistic Society of America
  13. 13.0 13.1 13.2 13.3 Five great Machine Translation Engines
  14. 14.0 14.1 14.2 14.3 How to Improve Machine Translation Quality
  15. 15.0 15.1 15.2 15.3 What is Neural Machine Translation & How does it work?
  16. 16.0 16.1 16.2 16.3 What are the best uses for machine translation?
  17. 17.0 17.1 17.2 17.3 meaning in the Cambridge English Dictionary
  18. 18.0 18.1 18.2 18.3 Science or Fiction: Machine Translation Explained
  19. 19.0 19.1 Machine Translation
  20. Configuring Machine Translation Engines
  21. 21.0 21.1 21.2 21.3 Machine Translation in 2020
  22. Neural machine translation with attention
  23. 23.0 23.1 23.2 23.3 A history of machine translation from the Cold War to deep learning
  24. 24.0 24.1 24.2 24.3 Machine Translation – 14 Current Applications and Services
  25. 25.0 25.1 25.2 25.3 The Disruptions of Neural Machine Translation – spheres
  26. 26.0 26.1 26.2 26.3 Transfer Learning Approaches for Machine Translation
  27. 27.0 27.1 27.2 27.3 Improving the Robustness of Neural Machine Translation to Social Media Text
  28. 28.0 28.1 28.2 28.3 Machine Translation and the Evaluation of Its Quality
  29. 29.0 29.1 29.2 29.3 Machine Translation: A Short Overview
  30. 30.0 30.1 30.2 30.3 Leverage machine translation with Phrase

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