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Helping Computers Find Meaning they Lost in Translation

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By Katie Kuksenok, March 7, 2012, 8 PM, downstairs at Town Hall Seattle.
AUDIO: http://www.engage-science.com/helping-computers-find-meaning-they-lost-in-translation/

Thousands of different languages are spoken around the world, limiting our ability to communicate globally. Translation can help to overcome this barrier, but is often prohibitively expensive and time consuming. Automatic translation can allow people to communicate ideas and experiences across languages and cultures, but current technologies still struggle with expressive language. Nuance, idiomatic expressions, and cultural references pose fundamental challenges to how machines translate from one language to another. However, people reading a translation can easily locate problematic wording, suggest possible corrections, and help to improve translation quality. In this talk, we explore how people and computers can work together to overcome language barriers.

Helping Computers Find Meaning they Lost in Translation

  1. 1. Helping Computers Find Meaning They Lost in Translation Katie Kuksenok UW Science Now! University of Washington March 8, 2012
  2. 2. Hi! I’m Katie! Computer Science and Engineering PhD student How people interact with computers Richness of written language
  3. 3. Posterity Tranquility Impeachment liable Indictment Concurrence
  4. 4. Google Searches By Language
  5. 5. Google Searches By Language
  6. 6. Machine Translation Source language
  7. 7. Machine Translation ----------------- ----------------- ----------------- ----------------- ----------------- --------- translation Source language Target language
  8. 8. Machine Translation + Interaction ----------------- ----------------- ----------------- ----------------- ----------------- --------- translation reading Source language Target language
  9. 9. Machine Translation + Interaction ----------------- ----------------- ----------------- ----------------- ----------------- --------- editing translation reading Source language Target language
  10. 10. Machine Translation + Interaction ----------------- ----------------- ----------------- ----------------- ----------------- --------- editing translation reading Source language Target language
  11. 11. The Translating Machine
  12. 12. The Translating Machine
  13. 13. Artificial Intelligence …where to begin? What would a person do? How can we describe that to a computer?
  14. 14. Rules & Dictionaries ?
  15. 15. Did it work?
  16. 16. Did it work? The spirit is willing, but the flesh is weak
  17. 17. Did it work? The spirit is willing, but the flesh is weak (Russian)
  18. 18. Did it work? The spirit is willing, but the flesh is weak The vodka is good, but the meat is rotten (Russian)
  19. 19. Rules & Dictionaries 
  20. 20. Rosetta Stone ?
  21. 21. Translate this: Source farok crrrok hihok yorok clok kantok yurp Target ____ ____ _____ _____ ____ ______ ___
  22. 22. Translate this: Source farok crrrok hihok yorok clok kantok yurp Target ____ ____ _____ _____ ____ ______ ___ Source wiwok farok izok stok Target sus clientes estan enfadados Source lalok farok ororok lalok sprok izok enemok Target los clientes y los asociados son enemigos Based on 2 parallel pairs:
  23. 23. Source wiwok farok izok stok Target sus clientes estan enfadados Source lalok farok ororok lalok sprok izok enemok Target los clientes y los asociados son enemigos Translate this: Source farok crrrok hihok yorok clok kantok yurp Target ____ ____ _____ _____ ____ ______ ___ Based on 2 parallel pairs:
  24. 24. Source wiwok farok izok stok Target sus clientes estan enfadados Source lalok farok ororok lalok sprok izok enemok Target los clientes y los asociados son enemigos Translate this: Source farok crrrok hihok yorok clok kantok yurp Target ____ ____ _____ _____ ____ ______ ___ Based on 2 parallel pairs:
  25. 25. Source wiwok farok izok stok Target sus clientes estan enfadados Source lalok farok ororok lalok sprok izok enemok Target los clientes y los asociados son enemigos Translate this: Source farok crrrok hihok yorok clok kantok yurp Target ____ ____ _____ _____ ____ ______ ___ Based on 2 parallel pairs: clientes
  26. 26. Machine Learning Statistics + Data = Surprisingly Good Results More Data + Better Data = Better Results
  27. 27. DATA
  28. 28. Statistical Machine Translation state of the art
  29. 29. 1954 1966 1991 2012
  30. 30. Posterity Tranquility Impeachment liable Indictment Concurrence
  31. 31. How do people learn different languages? By Instruction? By Example?
  32. 32. By Failing
  33. 33. By Failing Together
  34. 34. Interactive Machine Translation ?
  35. 35. Interactive Machine Translation Human Computation
  36. 36. Human Computation source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did.
  37. 37. Human Computation source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did. reorder [vixen] Saw a [vixen] he hungry crow been in the fig tree, and asked you why did. reorder [a] vixen Saw [a] the hungry crow been in the fig tree, and asked you why did. retype {A} vixen Saw the hungry crow been in the fig tree, and asked you why did. retype A vixen {saw} the hungry crow been in the fig tree, and asked you why did.
  38. 38. Human Computation source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did. reorder [vixen] Saw a [vixen] he hungry crow been in the fig tree, and asked you why did. reorder [a] vixen Saw [a] the hungry crow been in the fig tree, and asked you why did. retype {A} vixen Saw the hungry crow been in the fig tree, and asked you why did. retype A vixen {saw} the hungry crow been in the fig tree, and asked you why did. delete A vixen saw the hungry crow [been] in the fig tree, and asked you why did. delete A vixen saw the hungry crow in the fig tree, and asked [you] why did. replace A vixen saw the hungry crow in the fig tree, and asked [what was he doing] did.
  39. 39. Human Computation source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did. reorder [vixen] Saw a [vixen] he hungry crow been in the fig tree, and asked you why did. reorder [a] vixen Saw [a] the hungry crow been in the fig tree, and asked you why did. retype {A} vixen Saw the hungry crow been in the fig tree, and asked you why did. retype A vixen {saw} the hungry crow been in the fig tree, and asked you why did. delete A vixen saw the hungry crow [been] in the fig tree, and asked you why did. delete A vixen saw the hungry crow in the fig tree, and asked [you] why did. replace A vixen saw the hungry crow in the fig tree, and asked [what was he doing] did. retype A vixen saw the hungry crow in the fig tree, and asked {why} was he doing did. reorder A vixen saw the hungry crow in the fig tree, and asked why [he] was [he] doing did. retype A vixen saw the hungry crow in the fig tree, and asked why he was {there} did. delete A vixen saw the hungry crow in the fig tree, and asked why he was there [did]. final A vixen saw the hungry crow in the fig tree, and asked why he was there.
  40. 40. Human Computation source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did. reorder [vixen] Saw a [vixen] he hungry crow been in the fig tree, and asked you why did. [a] vixen Saw [a] the hungry crow been in the fig tree, and asked you why did. retype {A} vixen Saw the hungry crow been in the fig tree, and asked you why did. A vixen {saw} the hungry crow been in the fig tree, and asked you why did. delete A vixen saw the hungry crow [been] in the fig tree, and asked you why did. A vixen saw the hungry crow in the fig tree, and asked [you] why did. replace A vixen saw the hungry crow in the fig tree, and asked [what was he doing] did. retype A vixen saw the hungry crow in the fig tree, and asked {why} was he doing did. reorder A vixen saw the hungry crow in the fig tree, and asked why [he] was [he] doing did. retype A vixen saw the hungry crow in the fig tree, and asked why he was {there} did. delete A vixen saw the hungry crow in the fig tree, and asked why he was there [did]. final A vixen saw the hungry crow in the fig tree, and asked why he was there. x 100
  41. 41. Human Computation x 100… = Human Insight as Data source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did. reorder [vixen] Saw a [vixen] he hungry crow been in the fig tree, and asked you why did. [a] vixen Saw [a] the hungry crow been in the fig tree, and asked you why did. retype {A} vixen Saw the hungry crow been in the fig tree, and asked you why did. A vixen {saw} the hungry crow been in the fig tree, and asked you why did. delete A vixen saw the hungry crow [been] in the fig tree, and asked you why did. A vixen saw the hungry crow in the fig tree, and asked [you] why did. replace A vixen saw the hungry crow in the fig tree, and asked [what was he doing] did. retype A vixen saw the hungry crow in the fig tree, and asked {why} was he doing did. reorder A vixen saw the hungry crow in the fig tree, and asked why [he] was [he] doing did. retype A vixen saw the hungry crow in the fig tree, and asked why he was {there} did. delete A vixen saw the hungry crow in the fig tree, and asked why he was there [did]. final A vixen saw the hungry crow in the fig tree, and asked why he was there.
  42. 42. DATA
  43. 43. DATA  Human Insight as Data
  44. 44. Human Computation + Translation How can we capture the editing process ? source Vio una zorra al hambriento cuervo eternizado en la higuera, y le preguntó qué hacía. initial Saw a vixen the hungry crow been in the fig tree, and asked you why did. reorder [vixen] Saw a [vixen] he hungry crow been in the fig tree, and asked you why did. [a] vixen Saw [a] the hungry crow been in the fig tree, and asked you why did. retype {A} vixen Saw the hungry crow been in the fig tree, and asked you why did. A vixen {saw} the hungry crow been in the fig tree, and asked you why did. delete A vixen saw the hungry crow [been] in the fig tree, and asked you why did. A vixen saw the hungry crow in the fig tree, and asked [you] why did. replace A vixen saw the hungry crow in the fig tree, and asked [what was he doing] did. retype A vixen saw the hungry crow in the fig tree, and asked {why} was he doing did. reorder A vixen saw the hungry crow in the fig tree, and asked why [he] was [he] doing did. retype A vixen saw the hungry crow in the fig tree, and asked why he was {there} did. delete A vixen saw the hungry crow in the fig tree, and asked why he was there [did]. final A vixen saw the hungry crow in the fig tree, and asked why he was there.
  45. 45. Interaction Drag and drop to reorder Dropdowns with suggestions
  46. 46. Interaction Drag and drop to reorder vs Dropdowns with suggestions Free type
  47. 47. Interaction Suggested Alternative Translations
  48. 48. Many Hypotheses Interaction Suggested Alternative Translations
  49. 49. Interaction Suggested Alternative Translations
  50. 50. Interaction + Translation Translator expertise NoviceExpert
  51. 51. NoviceExpert Caitra (2010) Interaction + Translation Translator expertise
  52. 52. NoviceExpert Caitra (2010) TransType (2000) Interaction + Translation Translator expertise
  53. 53. NoviceExpert Google Translate Caitra (2010) TransType (2000) Interaction + Translation Translator expertise Google translate
  54. 54. data powers better systems NoviceExpert Google Translate Caitra (2010) Active Crowd Translation (2010) TransType (2000) Interaction + Translation Translator expertise
  55. 55. data powers better systems collaboration + discussion NoviceExpert Google Translate MonoTrans (2011)Caitra (2010) Active Crowd Translation (2010) TransType (2000) Interaction + Translation Translator expertise
  56. 56. Beyond Human Computation
  57. 57. Beyond Human Computation Munteanu, Baecker, and Penn, CHI 2008
  58. 58. Beyond Human Computation Duolingo.com
  59. 59. Interactive Machine Translation What if people interact with translation? How can a computer learn from that? How powerful is this approach?
  60. 60. Production: www.paperhand.org | Photos: mlight.typepad.com
  61. 61. A Sea of Information Production: www.paperhand.org | Photos: mlight.typepad.com
  62. 62. A City Obsessed with More and Newer Production: www.paperhand.org | Photos: mlight.typepad.com
  63. 63. An Insatiable, Hungry Monster Production: www.paperhand.org | Photos: mlight.typepad.com
  64. 64. An Insatiable, Hungry Monster Production: www.paperhand.org | Photos: mlight.typepad.com
  65. 65. An Insatiable, Hungry Monster Access? Production: www.paperhand.org | Photos: mlight.typepad.com
  66. 66. Effective Access Production: www.paperhand.org | Photos: mlight.typepad.com
  67. 67. Google Searches By Language
  68. 68. Thanks to: • Mentors – James Fogarty (UW) – Srinivas Bangalore (AT&T) – Luke Zettlemoyer (UW) – Dan Weld (UW) – Charlotte P. Lee (UW) – Jen Mankoff (CMU) • And many more – Study Participants – Colleagues – You! • Supporting Organizations – National Science Foundation – AT&T Labs – Microsoft Research – Google – University Book Store – Engage: The Science Speaker Series – University of Washington – Pacific Science Center – Town Hall Seattle – Microsoft
  69. 69. Thank You! Questions? Contact: kuksenok@cs.uw.edu Links, Citations, etc: students.washington.edu/kuksenok/engage
  • HaneeFawzul

    Jul. 13, 2016

By Katie Kuksenok, March 7, 2012, 8 PM, downstairs at Town Hall Seattle. AUDIO: http://www.engage-science.com/helping-computers-find-meaning-they-lost-in-translation/ Thousands of different languages are spoken around the world, limiting our ability to communicate globally. Translation can help to overcome this barrier, but is often prohibitively expensive and time consuming. Automatic translation can allow people to communicate ideas and experiences across languages and cultures, but current technologies still struggle with expressive language. Nuance, idiomatic expressions, and cultural references pose fundamental challenges to how machines translate from one language to another. However, people reading a translation can easily locate problematic wording, suggest possible corrections, and help to improve translation quality. In this talk, we explore how people and computers can work together to overcome language barriers.

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