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Apertium

From Wikipedia, the free encyclopedia
Apertium
Stable release
3.9.4[1] Edit this on Wikidata / 28 December 2023; 10 months ago (28 December 2023)
Repositorygithub.com/apertium
Written inC++
Operating systemPOSIX compatible and Windows NT (limited support)
Available in35 languages, see below
TypeRule-based machine translation
LicenseGNU General Public License
Websitewww.apertium.org

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.

Overview

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Apertium is a transfer-based machine translation system, which uses finite state transducers for all of its lexical transformations, and Constraint Grammar taggers as well as hidden Markov models or Perceptrons for part-of-speech tagging / word category disambiguation.[2] A structural transfer component is responsible for word movement and agreement; most Apertium language pairs up until now have used "chunking" or shallow transfer rules, though newer pairs use (possibly recursive) rules defined in a Context-free grammar.[3]

Many existing machine translation systems available at present are commercial or use proprietary technologies, which makes them very hard to adapt to new usages. Apertium code and data is free software and uses a language-independent specification, to allow for the ease of contributing to Apertium, more efficient development, and enhancing the project's overall growth.

At present (December 2020), Apertium has released 51 stable language pairs,[4] delivering fast translation with reasonably intelligible results (errors are easily corrected). Being an open-source project, Apertium provides tools for potential developers to build their own language pair and contribute to the project.

History

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Apertium originated as one of the machine translation engines in the project OpenTrad, which was funded by the Spanish government, and developed by the Transducens research group at the Universitat d'Alacant. It was originally designed to translate between closely related languages, although it has recently been expanded to treat more divergent language pairs. To create a new machine translation system, one just has to develop linguistic data (dictionaries, rules) in well-specified XML formats.

Language data developed for it (in collaboration with the Universidade de Vigo, the Universitat Politècnica de Catalunya and the Universitat Pompeu Fabra) currently support (in stable version) the Arabic, Aragonese, Asturian, Basque, Belarusian, Breton, Bulgarian, Catalan, Crimean Tatar, Danish, English, Esperanto, French, Galician, Hindi, Icelandic, Indonesian, Italian, Kazakh, Macedonian, Malaysian, Maltese, Northern Sami, Norwegian (Bokmål and Nynorsk), Occitan, Polish, Portuguese, Romanian, Russian, Sardinian, Serbo-Croatian, Silesian, Slovene, Spanish, Swedish, Tatar, Ukrainian, Urdu, and Welsh languages. A full list is available below. Several companies are also involved in the development of Apertium, including Prompsit Language Engineering, Imaxin Software and Eleka Ingeniaritza Linguistikoa.

The project has taken part in the 2009,[5] 2010,[6] 2011,[7] 2012,[8] 2013[9] and 2014[10] editions of Google Summer of Code and the 2010,[11] 2011,[12] 2012,[13] 2013,[14] 2014,[15] 2015,[16] 2016[17] and 2017[18] editions of Google Code-In.

Translation methodology

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Pipeline of Apertium machine translation system

This is an overall, step-by-step view how Apertium works.

The diagram displays the steps that Apertium takes to translate a source-language text (the text we want to translate) into a target-language text (the translated text).

  1. Source language text is passed into Apertium for translation.
  2. The deformatter removes formatting markup (HTML, RTF, etc.) that should be kept in place but not translated.
  3. The morphological analyser segments the text (expanding elisions, marking set phrases, etc.), and looks up segments in the language dictionaries, returning dictionary forms and tags for all matches. In pairs that involve agglutinative morphology, including a number of Turkic languages, a Helsinki Finite State Transducer (HFST) is used. Otherwise, an Apertium-specific finite state transducer system called lttoolbox,[19] is used.
  4. The morphological disambiguator (the morphological analyser and the morphological disambiguator together form the part of speech tagger) resolves ambiguous segments (i.e., when there is more than one match) by choosing one match. Apertium uses Constraint Grammar rules (with the vislcg3 parser[20]) for most of its language pairs.
  5. Retokenisation uses a finite state transducer to match sequences of lexical units and may reorder or translate tags (often used for translating idiomatic expressions into something that more approaches the target language grammar)
  6. Lexical transfer looks up disambiguated source-language basewords to find their target-language equivalents (i.e., mapping source language to target language). For lexical transfer, Apertium uses an XML-based dictionary format called bidix.[21]
  7. Lexical selection chooses between alternative translations when the source text word has alternative meanings. Apertium uses a specific XML-based technology, apertium-lex-tools,[22] to perform lexical selection.
  8. Structural transfer (i.e., it is an XML format that allows writing complex structural transfer rules) can consist of one-step chunking transfer, three-step chunking transfer or a CFG-based transfer module. The chunking modules flag grammatical differences between the source language and target language (e.g. gender or number agreement) by creating a sequence of chunks containing markers for this. They then reorder or modify chunks in order to produce a grammatical translation in the target-language. The newer CFG-based module matches input sequences into possible parse trees, selecting the best-ranking one and applying transformation rules on the tree.
  9. The morphological generator uses the tags to deliver the correct target language surface form. The morphological generator is a morphological transducer,[23] just like the morphological analyser. A morphological transducer both analyses and generates forms.
  10. The post-generator makes any necessary orthographic changes due to the contact of words (e.g. elisions).
  11. The reformatter replaces formatting markup (HTML, RTF, etc.) that was removed by the deformatter in the first step.
  12. Apertium delivers the target-language translation.

Supported languages

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As of November 2024, the following 108 pairs and 50 languages and languages varieties are supported by Apertium.

  1. Afrikaans to Dutch
  2. Arabic to Maltese
  3. Aragonese to Catalan
  4. Aragonese to Spanish
  5. Arpitan (Franco-Provençal) to French
  6. Basque to English
  7. Basque to Spanish
  8. Belarusian to Russian
  9. Breton to French
  10. Bulgarian to Macedonian
  11. Catalan to Aragonese
  12. Catalan to English
  13. Catalan to Esperanto
  14. Catalan to French
  15. Catalan to Italian
  16. Catalan to Occitan
  17. Catalan to Aranese
  18. Catalan to Portuguese
  19. Catalan to Brazilian Portuguese
  20. Catalan to European Portuguese (traditional spelling)
  21. Catalan to Romanian
  22. Catalan to Sardinian
  23. Catalan to Spanish
  24. Crimean Tatar to Turkish
  25. Danish to Norwegian (Bokmål)
  26. Danish to Norwegian (Nynorsk)
  27. Danish to Swedish
  28. Dutch to Afrikaans
  29. English to Catalan
  30. English to Valencian
  31. English to Esperanto
  32. English to Galician
  33. English to Serbo-Croatian
  34. English to Spanish
  35. Esperanto to English
  36. French to Arpitan (Franco-Provençal)
  37. French to Catalan
  38. French to Esperanto
  39. French to Occitan
  40. French to Gascon
  41. French to Spanish
  42. Galician to English
  43. Galician to Portuguese
  44. Galician to Spanish
  45. Hindi to Urdu
  46. Icelandic to English
  47. Icelandic to Swedish
  48. Indonesian to Malay
  49. Italian to Catalan
  50. Italian to Sardinian
  51. Italian to Spanish
  52. Kazakh to Tatar
  53. Macedonian to Bulgarian
  54. Macedonian to English
  55. Malay to Indonesian
  56. Maltese to Arabic
  57. Northern Sámi to Norwegian (Bokmål)
  58. Norwegian (Bokmål) to Danish
  59. Norwegian (Bokmål) to Norwegian (Nynorsk)
  60. Norwegian (Bokmål) to East Norwegian, vi→vi
  61. Norwegian (Bokmål) to Swedish
  62. Norwegian (Nynorsk) to Danish
  63. Norwegian (Nynorsk) to Norwegian (Bokmål)
  64. Norwegian (Nynorsk) to East Norwegian, vi→vi
  65. Norwegian (Nynorsk) to Swedish
  66. East Norwegian, vi→vi to Norwegian (Nynorsk)
  67. Occitan to Catalan
  68. Occitan to French
  69. Occitan to Spanish
  70. Aranese to Catalan
  71. Aranese to Spanish
  72. Gascon to French
  73. Polish to Silesian
  74. Portuguese to Catalan
  75. Portuguese to Galician
  76. Portuguese to Spanish
  77. Romanian to Catalan
  78. Romanian to Spanish
  79. Russian to Belarusian
  80. Russian to Ukrainian
  81. Sardinian to Italian
  82. Serbo-Croatian to English
  83. Serbo-Croatian to Macedonian
  84. Serbo-Croatian to Slovenian
  85. Silesian to Polish
  86. Slovenian to Serbo-Croatian
  87. Spanish to Aragonese
  88. Spanish to Asturian
  89. Spanish to Catalan
  90. Spanish to Valencian
  91. Spanish to English
  92. Spanish to Esperanto
  93. Spanish to French
  94. Spanish to Galician
  95. Spanish to Italian
  96. Spanish to Occitan
  97. Spanish to Aranese
  98. Spanish to Portuguese
  99. Spanish to Brazilian Portuguese
  100. Swedish to Danish
  101. Swedish to Icelandic
  102. Swedish to Norwegian (Bokmål)
  103. Swedish to Norwegian (Nynorsk)
  104. Tatar to Kazakh
  105. Turkish to Crimean Tatar
  106. Ukrainian to Russian
  107. Urdu to Hindi
  108. Welsh to English

See also

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Notes

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  1. ^ . 28 December 2023 https://github.com/apertium/apertium/releases/tag/v3.9.4. {{cite web}}: Missing or empty |title= (help)
  2. ^ Francis M. Tyers (2010) "Rule-based Breton to French machine translation Archived 2016-11-17 at the Wayback Machine". 'Proceedings of the 14th Annual Conference of the European Association of Machine Translation, EAMT10', pp. 174--181
  3. ^ Khanna, Tanmai; Washington, Jonathan N.; Tyers, Francis M.; Bayatlı, Sevilay; Swanson, Daniel G.; Pirinen, Tommi A.; Tang, Irene; Alòs i Font, Hèctor (1 December 2021). "Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages". Machine Translation. 35 (4): 475–502. doi:10.1007/s10590-021-09260-6. hdl:10037/22990.
  4. ^ "Apertium".
  5. ^ "Accepted organizations for Google Summer of Code 2009".
  6. ^ "Accepted organizations for Google Summer of Code 2010".
  7. ^ "Accepted organizations for Google Summer of Code 2011".
  8. ^ "Accepted organizations for Google Summer of Code 2012".
  9. ^ "Accepted organizations for Google Summer of Code 2013".
  10. ^ "Accepted organizations for Google Summer of Code 2014".
  11. ^ "Accepted organizations for Google Code-in 2010".
  12. ^ "Accepted organizations for Google Code-in 2011".
  13. ^ "Accepted organizations for Google Code In 2012".
  14. ^ "Accepted organizations for Google Code-in 2013".
  15. ^ "Accepted organizations for Google Code-in 2014".
  16. ^ "Accepted organizations for Google Code-in 2015".
  17. ^ "Accepted organizations for Google Code-in 2016".
  18. ^ "Accepted organizations for Google Code-in 2017".
  19. ^ "Lttoolbox - Apertium". wiki.apertium.org. Retrieved 2016-01-19.
  20. ^ "VISL". beta.visl.sdu.dk. Retrieved 2016-01-19.
  21. ^ "Bilingual dictionary - Apertium". wiki.apertium.org. Retrieved 2016-01-19.
  22. ^ "Constraint-based lexical selection module - Apertium". wiki.apertium.org. Retrieved 2016-01-19.
  23. ^ "Morphological dictionary - Apertium". wiki.apertium.org. Retrieved 2016-01-19.

References

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  • Corbí-Bellot, M. et al. (2005) "An open-source shallow-transfer machine translation engine for the romance languages of Spain" in Proceedings of the European Association for Machine Translation, 10th Annual Conference, Budapest 2005, pp. 79–86
  • Armentano-Oller, C. et al. (2006) "Open-source Portuguese-Spanish machine translation" in Lecture Notes in Computer Science 3960 [Computational Processing of the Portuguese Language, Proceedings of the 7th International Workshop on Computational Processing of Written and Spoken Portuguese, PROPOR 2006], p 50–59.
  • Forcada, M. L. et al. (2010) "Documentation of the Open-Source Shallow-Transfer Machine Translation Platform Apertium" in Departament de Llenguatges i Sistemes Informatics, University of Alacant.
  • Forcada, M. L. et al. (2011) "Apertium: a free/open-source platform for rule-based machine translation". in "doi:10.1007/s10590-011-9090-0
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End-user services and software

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(All services are based on the Apertium engine)

Online translation websites

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Offline applications

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