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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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  • [{'LOWER': 'machine'}, {'LEMMA': 'translation'}]
  • [{'LEMMA': 'MT'}]
  • [{'LOWER': 'machine'}, {'LEMMA': 'translation'}]
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