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Robert MunroStanford University & Energy for OpportunityWorkshop on Crowdsourcing and Translation10 June 2010Realtime crowdsourced translation for emergency response and beyond
Where I’ve sourced crowds	IndustryResearchEsp Psycholinguistics Munro et al 2010)Social development
Daily potential language exposureOn a given day, what is the average number of languages that someone could potentially hear?How has this changed?# of languagesYear
Daily potential language exposure# of languagesYear
Daily potential language exposure# of languagesYear
Daily potential language exposure# of languagesYear
Daily potential language exposureYou can make a realtime difference in the life of anyone you can communicate withWe will never be so under-resourced to do this as we are right nowBut we can leverage many of the same technologies to crowdsource
Realtime crowdsourced translation for emergency response and beyond
Haiti, January 12Text messaging (SMS) is the dominant form of remote communication in HaitiThe existing emergency response systems failedMost text messages were getting throughA team of people came together quickly, all connected by Josh Nesbit of FrontlineSMS:Medic, to  put together an SMS-based emergency response systemWe launched in under 48 hours
The responders don’t speak KreyolFanmimwennanKafou, 24 Cote Plage, 41A bezwenmanjeakdloMounkwensenanSakreKènanPòtoprensTi ekipmanLopital General genyenyopakaminmfè 24 èFanm gen tranche poufè yon pititnanDelmas 31
The responders don’t speak KreyolFanmimwennanKafou, 24 Cote Plage, 41A bezwenmanjeakdloMounkwensenanSakreKènanPòtoprensTi ekipmanLopital General genyenyopakaminmfè 24 èFanm gen tranche poufè yon pititnanDelmas 31My family in Carrefour, 24 Cote Plage,41A needs food and water
People trapped in Sacred Heart Church, PauP
General Hospital has less than 24 hrs. supplies
Undergoing children delivery Delmas 31Making a difference, from anywhereTranslate 1 message = help 1 person
Messages: ~1000 per day
The process
The process
The processAverage turnaround = 10 mins
At launch – utilizing volunteersCoordinate responseResponseMessage to ‘4636’Translation and geo-location@TuftsRefine coordinates and identify actionable itemsVolunteer translators
Today – creating jobsCoordinate responseResponseMessage to ‘4636 / 177’Translation and geo-location@TuftsRefine coordinates and identify actionable itemsPaid workers in Haiti
Realtime crowdsourced translation for emergency response and beyond
Collaborating organizations
Local knowledge countsApoDalilaHaiti responders(18.4957, -72.3185)Workers collaborating to find locations:Dalila: I need Thomassin Apo pleaseApo: Kenscoff Route: Lat: 18.495746829274168, Long:-72.31849193572998Apo: This Area after Petion-Ville and Pelerin 5 is not on Google Map. We have no streets nameFeedback from responders:"just got emergency SMS, child delivery,USCG are acting, and, the GPS coordinates of the location we got from someone of your team were 100% accurate!"The ability for someone to make a real-time difference at any other place in the world:Apo: I know this place like my pocketDalila: thank God u was here‘here’ = anywhere
Local knowledge counts“Rue Casseus no 9 gen yon santkap bay swenakmounkiblese e mounkibrile”Street Casseus no 9, there is a center that helps people that are wounded or burntMarc, Union Haiti:“I will pass it on that is my cousins hospital.”
A person alone is not in a crowd
Ushahidi – Jan 14
Ushahidi – Jan 17
Ushahidi – Jan 17
Feedback - respondersClark Craig of the Marine Corps:
“I cannot overemphasize to you what the work of the Ushahidi/Haiti has provided. It is saving lives every day.”
Secretary of State Hillary Clinton:
“The technology community has set up interactive maps to help us identify needs and target resources. And on Monday, a seven-year-old girl and two women were pulled from the rubble of a collapsed supermarket by an American search-and-rescue team after they sent a text message calling for help.”
Craig Fulgate, FEMA Task Force:
“[The] Crisis Map of Haiti represents the most comprehensive and up-to-date map available to the humanitarian community.”
Ushahidi@Tufts
“The World Food Program delivered food to an informal camp of 2500 people, having yet to receive food or water, in Diquini to a location that 4636 had identified for them.”>1000 VolunteersApo:“When I found out about the project I thought it was a really good thing because I could help people, instead of just donating to charity”Sarah, Union Haiti:“You kept us going”
Crowdsourced mapping – Jan 12
Crowdsourced mapping – Jan 23

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Realtime crowdsourced translation for emergency response and beyond

  • 1. Robert MunroStanford University & Energy for OpportunityWorkshop on Crowdsourcing and Translation10 June 2010Realtime crowdsourced translation for emergency response and beyond
  • 2. Where I’ve sourced crowds IndustryResearchEsp Psycholinguistics Munro et al 2010)Social development
  • 3. Daily potential language exposureOn a given day, what is the average number of languages that someone could potentially hear?How has this changed?# of languagesYear
  • 4. Daily potential language exposure# of languagesYear
  • 5. Daily potential language exposure# of languagesYear
  • 6. Daily potential language exposure# of languagesYear
  • 7. Daily potential language exposureYou can make a realtime difference in the life of anyone you can communicate withWe will never be so under-resourced to do this as we are right nowBut we can leverage many of the same technologies to crowdsource
  • 9. Haiti, January 12Text messaging (SMS) is the dominant form of remote communication in HaitiThe existing emergency response systems failedMost text messages were getting throughA team of people came together quickly, all connected by Josh Nesbit of FrontlineSMS:Medic, to put together an SMS-based emergency response systemWe launched in under 48 hours
  • 10. The responders don’t speak KreyolFanmimwennanKafou, 24 Cote Plage, 41A bezwenmanjeakdloMounkwensenanSakreKènanPòtoprensTi ekipmanLopital General genyenyopakaminmfè 24 èFanm gen tranche poufè yon pititnanDelmas 31
  • 11. The responders don’t speak KreyolFanmimwennanKafou, 24 Cote Plage, 41A bezwenmanjeakdloMounkwensenanSakreKènanPòtoprensTi ekipmanLopital General genyenyopakaminmfè 24 èFanm gen tranche poufè yon pititnanDelmas 31My family in Carrefour, 24 Cote Plage,41A needs food and water
  • 12. People trapped in Sacred Heart Church, PauP
  • 13. General Hospital has less than 24 hrs. supplies
  • 14. Undergoing children delivery Delmas 31Making a difference, from anywhereTranslate 1 message = help 1 person
  • 19. At launch – utilizing volunteersCoordinate responseResponseMessage to ‘4636’Translation and geo-location@TuftsRefine coordinates and identify actionable itemsVolunteer translators
  • 20. Today – creating jobsCoordinate responseResponseMessage to ‘4636 / 177’Translation and geo-location@TuftsRefine coordinates and identify actionable itemsPaid workers in Haiti
  • 23. Local knowledge countsApoDalilaHaiti responders(18.4957, -72.3185)Workers collaborating to find locations:Dalila: I need Thomassin Apo pleaseApo: Kenscoff Route: Lat: 18.495746829274168, Long:-72.31849193572998Apo: This Area after Petion-Ville and Pelerin 5 is not on Google Map. We have no streets nameFeedback from responders:"just got emergency SMS, child delivery,USCG are acting, and, the GPS coordinates of the location we got from someone of your team were 100% accurate!"The ability for someone to make a real-time difference at any other place in the world:Apo: I know this place like my pocketDalila: thank God u was here‘here’ = anywhere
  • 24. Local knowledge counts“Rue Casseus no 9 gen yon santkap bay swenakmounkiblese e mounkibrile”Street Casseus no 9, there is a center that helps people that are wounded or burntMarc, Union Haiti:“I will pass it on that is my cousins hospital.”
  • 25. A person alone is not in a crowd
  • 29. Feedback - respondersClark Craig of the Marine Corps:
  • 30. “I cannot overemphasize to you what the work of the Ushahidi/Haiti has provided. It is saving lives every day.”
  • 31. Secretary of State Hillary Clinton:
  • 32. “The technology community has set up interactive maps to help us identify needs and target resources. And on Monday, a seven-year-old girl and two women were pulled from the rubble of a collapsed supermarket by an American search-and-rescue team after they sent a text message calling for help.”
  • 33. Craig Fulgate, FEMA Task Force:
  • 34. “[The] Crisis Map of Haiti represents the most comprehensive and up-to-date map available to the humanitarian community.”
  • 36. “The World Food Program delivered food to an informal camp of 2500 people, having yet to receive food or water, in Diquini to a location that 4636 had identified for them.”>1000 VolunteersApo:“When I found out about the project I thought it was a really good thing because I could help people, instead of just donating to charity”Sarah, Union Haiti:“You kept us going”
  • 39. Preparing for the futureMicrosoft Research have built new MT models with the SMS (Lewis 2010)Issues for MTNeeded to re-translated (too much paraphrasing)Needed to normalize (too many abbreviations)
  • 40. NLP for social developmentBantuBeyond Haiti and emergency responseThose with the least resources have the most to gainChichewa
  • 41. Professional communicationsPartnering with FrontlineSMS:Medic and a clinic in rural MalawiClassifying medical text messages (Munro and Manning 2010)
  • 42. Why apply NLP to medical SMS?Identify outbreaks earlyMalaria Prevalence Model, MARARoute important data quicklySave time2 doctors for 250,000 patients= 5 seconds per patient per year
  • 43. Variation is the normKeyword-based models are suboptimal
  • 45. The potentialmedicine, banking, market information, literacy …# of languagesYear
  • 46. Thank youReferencesLewis, Will. 2010. Haitian Creole: How to Build and Ship an MT Engine from Scratch in 4 days, 17 hours, & 30 minutes. Annual Meeting of the European Association for Machine TranslationMunro, Robert. 2010. Haiti emergency response: the power of crowdsourcing and SMS. Relief 2.0 in HaitiMunro, Robert, Steven Bethard, Victor Kuperman, Vicky Tzuyin Lai, Robin Melnick, Christopher Potts, Tyler Schnoebelen and Harry Tily. 2010. Crowdsourcing and language studies: the new generation of linguistic data. Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk.Munro, Robert and Christopher Manning. 2010. Subword Variation in Text Message Classification. Annual Conference of the North American Chapter of the Association for Computational Linguistics