1. Robert Munro Stanford University & Energy for Opportunity Workshop on Crowdsourcing and Translation 10 June 2010 Realtime crowdsourced translation for emergency response and beyond

2. Where I’ve sourced crowds Industry Research Esp Psycholinguistics Munro et al 2010) Social development

3. Daily potential language exposure On a given day, what is the average number of languages that someone could potentially hear? How has this changed? # of languages Year

4. Daily potential language exposure # of languages Year

5. Daily potential language exposure # of languages Year

6. Daily potential language exposure # of languages Year

7. Daily potential language exposure You can make a realtime difference in the life of anyone you can communicate with We will never be so under-resourced to do this as we are right now But we can leverage many of the same technologies to crowdsource

9. Haiti, January 12 Text messaging (SMS) is the dominant form of remote communication in Haiti The existing emergency response systems failed Most text messages were getting through A team of people came together quickly, all connected by Josh Nesbit of FrontlineSMS:Medic, to put together an SMS-based emergency response system We launched in under 48 hours

10. The responders don’t speak Kreyol FanmimwennanKafou, 24 Cote Plage, 41A bezwenmanjeakdlo MounkwensenanSakreKènanPòtoprens Ti ekipmanLopital General genyenyopakaminmfè 24 è Fanm gen tranche poufè yon pititnanDelmas 31

12. People trapped in Sacred Heart Church, PauP

13. General Hospital has less than 24 hrs. supplies

15. Messages: ~1000 per day

16. The process

17. The process

18. The process Average turnaround = 10 mins

19. At launch – utilizing volunteers Coordinate response Response Message to ‘4636’ Translation and geo-location @Tufts Refine coordinates and identify actionable items Volunteer translators

20. Today – creating jobs Coordinate response Response Message to ‘4636 / 177’ Translation and geo-location @Tufts Refine coordinates and identify actionable items Paid workers in Haiti

22. Collaborating organizations

23. Local knowledge counts Apo Dalila Haiti responders (18.4957, -72.3185) Workers collaborating to find locations: Dalila: I need Thomassin Apo please Apo: Kenscoff Route: Lat: 18.495746829274168, Long:-72.31849193572998 Apo: This Area after Petion-Ville and Pelerin 5 is not on Google Map. We have no streets name Feedback 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 pocket Dalila: 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 burnt Marc, Union Haiti: “I will pass it on that is my cousins hospital.”

25. A person alone is not in a crowd

26. Ushahidi – Jan 14

27. Ushahidi – Jan 17

28. Ushahidi – Jan 17

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.”

35. Ushahidi@Tufts

37. Crowdsourced mapping – Jan 12

38. Crowdsourced mapping – Jan 23

39. Preparing for the future Microsoft Research have built new MT models with the SMS (Lewis 2010) Issues for MT Needed to re-translated (too much paraphrasing) Needed to normalize (too many abbreviations)

40. NLP for social development Bantu Beyond Haiti and emergency response Those with the least resources have the most to gain Chichewa

41. Professional communications Partnering with FrontlineSMS:Medic and a clinic in rural Malawi Classifying medical text messages (Munro and Manning 2010)

42. Why apply NLP to medical SMS? Identify outbreaks early Malaria Prevalence Model, MARA Route important data quickly Save time 2 doctors for 250,000 patients = 5 seconds per patient per year

43. Variation is the norm Keyword-based models are suboptimal

44. Example

45. The potential medicine, banking, market information, literacy … # of languages Year

46. Thank you References Lewis, 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 Translation Munro, Robert. 2010. Haiti emergency response: the power of crowdsourcing and SMS. Relief 2.0 in Haiti Munro, 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