1887
Volume 8, Issue 2
  • ISSN 2211-3711
  • E-ISSN: 2211-372X
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Abstract

Abstract

The application of machine translation (MT) in crisis settings is of increasing interest to humanitarian practitioners. We collaborated with industry and non-profit partners: (1) to develop and test the utility of an MT system trained specifically on crisis-related content in an under-resourced language combination (French-to-Swahili); and (2) to evaluate the extent to which speakers of both French and Swahili without post-editing experience could be mobilized to post-edit the output of this system effectively. Our small study carried out in Kenya found that our system performed well, provided useful output, and was positively evaluated by inexperienced post-editors. We use the study to discuss the feasibility of MT use in crisis settings for low-resource language combinations and make recommendations on data selection and domain consideration for future crisis-related MT development.

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2019-11-05
2019-11-22
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References

  1. Al-Dahash, Hajer, Menaha Thayaparan, and Udayangani Kulatunga
    2016 “Understanding the Terminologies: Disaster, Crisis and Emergency.” InProceedings of the 32nd Association of Researchers in Construction Management (ARCOM), 1191–1200. Manchester, UK: Association of Researchers in Construction Management.
    [Google Scholar]
  2. Alexander, David
    2002Principles of Emergency Planning and Management. Oxford: Oxford University Press.
    [Google Scholar]
  3. Ansari, Aimee, and Rebecca Petras [Google Scholar]
  4. Cadwell, Patrick
    2016 “A Place for Translation Technologies in Disaster Settings: The Case of the 2011 Great East Japan Earthquake.” InConflict and Communication: A Changing Asia in a Globalising World, edited byMinako O’Hagan, and Qi Zhang, 169–194. New York: Nova Science Publishers.
    [Google Scholar]
  5. Cadwell, Patrick, and Sharon O’Brien
    2016 “Language, Culture, and Translation in Disaster ICT: An Ecosystemic Model of Understanding.” Perspectives24 (4): 557–575. doi:  10.1080/0907676X.2016.1142588
    https://doi.org/10.1080/0907676X.2016.1142588 [Google Scholar]
  6. Castilho, Sheila, Sharon O’Brien, Fabio Alves, and Morgan O’Brien
    2014 “Does Post-Editing Increase Usability? A Study with Brazilian Portuguese as Target Language.” InProceedings of the 17th Conference of the European Association for Machine Translation, edited byMarko Tadić, Philipp Koehn, Johann Roturier, and Andy Way, 183–190. Dubrovnik: EAMT.
    [Google Scholar]
  7. Castilho, Sheila, Stephen Doherty, Federico Gaspari, and Joss Moorkens
    2018 “Approaches to Human and Machine Translation Quality Assessment.” InTranslation Quality Assessment: From Principles to Practice, edited byJoss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 9–38. Cham: Switzerland. 10.1007/978‑3‑319‑91241‑7_2
    https://doi.org/10.1007/978-3-319-91241-7_2 [Google Scholar]
  8. Chu, Chenhui, and Rui Wang
    2018 “A Survey of Domain Adaptation for Neural Machine Translation.” InProceedings of the 27th International Conference on Computational Linguistics, edited byEmily M. Bender, Leon Derczynski, and Pierre Isabelle, 1304–1319. Santa Fe, New Mexico: Association for Computational Linguistics, aclweb.org/anthology/C18-1
    [Google Scholar]
  9. Cruz Silva, Catarina, Chao-Hong Liu, Alberto Poncelas, and Andy Way
    2018 “Extracting In-Domain Training Data for Neural Machine Translation Using Data Selection Methods.” InProceedings of the Third Conference on Machine Translation, 224–231. Brussels, Belgium: Association for Computational Linguistics, www.statmt.org/wmt18/WMT-2018.pdf
    [Google Scholar]
  10. Doherty, Stephen, and Sharon O’Brien
    2014 “Assessing the Usability of Raw Machine Translated Output: A User-Centered Study Using Eye Tracking.” International Journal of Human-Computer Interaction30 (1): 40–51. doi:  10.1080/10447318.2013.802199
    https://doi.org/10.1080/10447318.2013.802199 [Google Scholar]
  11. Federici, Federico M.
    (ed) 2016Mediating Emergencies and Conflicts. Houndmills: Palgrave Macmillan. 10.1057/978‑1‑137‑55351‑5
    https://doi.org/10.1057/978-1-137-55351-5 [Google Scholar]
  12. Federici, Federico M., and Patrick Cadwell
    2018 “Training Citizen Translators: Design and Delivery of Bespoke Training on the Fundamentals of Translation for New Zealand Red Cross.” Translation Spaces7 (1): 23–43. doi:  10.1075/ts.00002.fed
    https://doi.org/10.1075/ts.00002.fed [Google Scholar]
  13. Federici, Federico M., Brian J. Gerber, Sharon O’Brien, and Patrick Cadwell
    2019The International Humanitarian Sector and Language Translation in Crisis Situations. Assessment of Current Practices and Future Needs. London; Dublin; Phoenix, AZ: INTERACT The International Network on Crisis Translation.
    [Google Scholar]
  14. Fischer, Henry W.
    2008Response to Disaster: Fact versus Fiction and Its Perpetuation: The Sociology of Disaster. Lanham, MD: University Press of America.
    [Google Scholar]
  15. Flanagan, Marian, and Tina Paulsen Christensen
    2014 “Testing Post-Editing Guidelines: How Translation Trainees Interpret Them and How to Tailor Them for Translator Training Purposes.” The Interpreter and Translator Trainer8 (2): 257–275. doi:  10.1080/1750399X.2014.936111
    https://doi.org/10.1080/1750399X.2014.936111 [Google Scholar]
  16. Gaspari, Federico, Antonio Toral, Sudip Kumar Naskar, Declan Groves, and Andy Way
    2014 “Perception vs Reality: Measuring Machine Translation Post-Editing Productivity.” InProceedings of the 11th Conference of the Association for Machine Translation in the Americas: Workshop on Post-Editing Technology and Practice (WPTP3), edited bySharon O’Brien, Michel Simard, and Lucia Specia, 60–72. Vancouver: AMTA.
    [Google Scholar]
  17. Guerberof Arenas, Ana
    2009 “Productivity and Quality in the Post-editing of Outputs from Translation Memories and Machine Translation.” Localisation Focus7 (1): 11–21.
    [Google Scholar]
  18. Haddow, George D., Jane A. Bullock, and Damon P. Coppola
    2011Introduction to Emergency Management. Burlington, MA: Butterworth Heinemann.
    [Google Scholar]
  19. Harvard Humanitarian Initiative
    Harvard Humanitarian Initiative 2011Disaster Relief 2.0: The Future of Information Sharing in Humanitarian Emergencies. Washington, D.C. and Berkshire, UK: UN Foundation & Vodafone Foundation Technology Partnership.
    [Google Scholar]
  20. IDMC (Internal Displacement Monitoring Centre)
    IDMC (Internal Displacement Monitoring Centre) 2018Global Report on Internal Displacement 2018. AccessedMarch 3, 2019. www.internal-displacement.org/global-report/grid2018/
    [Google Scholar]
  21. Karakanta, Alina, Jon Dehdari, Josef van, Genabith J.
    2018 “Neural Machine Translation for Low-Resource Languages without Parallel Corpora.” Machine Translation32 (1–2), 167–189. doi:  10.1007/s10590‑017‑9203‑5
    https://doi.org/10.1007/s10590-017-9203-5 [Google Scholar]
  22. Kobus, Catherine, Josep Crego, and Jean Senellart
    2017 “Domain Control for Neural Machine Translation.” InProceedings of the International Conference Recent Advances in Natural Language Processing, RANLP, 372–378. Varna, Bulgaria: Association for Computational Linguistics. doi:  10.26615/978‑954‑452‑049‑6_049
    https://doi.org/10.26615/978-954-452-049-6_049 [Google Scholar]
  23. Koehn, Philipp, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst
    2007 “Moses: Open Source Toolkit for Statistical Machine Translation.” InProceedings of the 45th Annual Meeting of the Association for Computational Linguistics, 177–180. Prague, Czech Republic: Association for Computational Linguistics.
    [Google Scholar]
  24. Koponen, Maarit
    2012 “Comparing Human Perceptions of Post-Editing Effort with Post-Editing Operations.” InProceedings of the 7th Workshop on Statistical Machine Translation, 181–190. New York: Association for Computational Linguistics.
    [Google Scholar]
  25. 2015 “How to Teach Machine Translation Post-Editing? Experiences from a Post-Editing Course.” InProceedings of the 4th Workshop on Post-Editing Technology and Practice (WPTP4), 2–15. Miami: Association for Computational Linguistics.
    [Google Scholar]
  26. Lewis, William D.
    2010 “Haitian Creole: How to Build and Ship an MT Engine from Scratch in 4 Days, 17 Hours, & 30 Minutes.” InProceedings of the 14th Annual Conference of the European Association for Machine Translation (EAMT 2010) (no pagination). Saint-Raphaël, France: EAMT. AccessedJuly 7, 2019. www.mt-archive.info/EAMT-2010-Lewis.pdf
    [Google Scholar]
  27. Lewis, William D., Robert Munro, and Stephan Vogel
    2011 “Crisis MT: Developing a Cookbook for MT in Crisis Situations.” InProceedings of the 6th Workshop on Statistical Machine Translation, 501–511. Edinburgh, Scotland: UK Association for Computational Linguistics.
    [Google Scholar]
  28. Liu, Chao-Hong
    2018Workshop Proceedings of Technologies for MT of Low Resource Languages (LoResMT 2018). AccessedMarch 3, 2019. aclweb.org/anthology/W18-2200
    [Google Scholar]
  29. Liu, Chao-Hong, Catarina Cruz Silva, Longyue Wang, and Andy Way
    2018 “Pivot Machine Translation Using Chinese as Pivot Language.” InProceedings of the 14th China Workshop on Machine Translation, 1–12. Wuyishan, China: Springer Nature Singapore Pte Ltd. doi:  10.1007/978‑981‑13‑3083‑4_7
    https://doi.org/10.1007/978-981-13-3083-4_7 [Google Scholar]
  30. Mehra, Kanav, and Vibhash Chandra
    2017 “Summarizing Microblogs for Emergency Relief and Preparedness.” InProceedings of the First International Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2017), 104–108. Aberdeen, UK: CEUR. AccessedJuly 7, 2019. ceur-ws.org/Vol-1832/
    [Google Scholar]
  31. Moorkens, Joss, Sharon O’Brien, Igor A. L. da Silva, Norma B. de Lima Fonseca, and Fabio Alves
    2015 “Correlations of Perceived Post-Editing Effort with Measurements of Actual Effort.” Machine Translation29 (3–4): 267–284. doi:  10.1007/s10590‑015‑9175‑2
    https://doi.org/10.1007/s10590-015-9175-2 [Google Scholar]
  32. O’Brien, Sharon
    . Forthcoming. “Translation Technology and Disaster Management.” InThe Routledge Handbook of Translation and Technology edited by Minako O’Hagan. Abingdon, Oxon: Routledge. 10.4324/9781315311258‑18
    https://doi.org/10.4324/9781315311258-18 [Google Scholar]
  33. O’Brien, Sharon, and Patrick Cadwell
    2017 “Translation Facilitates Comprehension of Health-Related Crisis Information: Kenya as an Example.” The Journal of Specialised Translation28: 23–51. AccessedJuly 7, 2019. https://www.jostrans.org/issue28/art_obrien.pdf
    [Google Scholar]
  34. O’Brien, Sharon, Federico M. Federici, Patrick Cadwell, Jay Marlowe, and Brain Gerber
    2018 “Language Translation During Disaster: A Comparative Analysis of Five National Approaches.” International Journal for Disaster Risk Reduction31: 627–636. 10.1016/j.ijdrr.2018.07.006
    https://doi.org/10.1016/j.ijdrr.2018.07.006 [Google Scholar]
  35. Och, Franz Josef, and Hermann Ney
    2003 “A Systematic Comparison of Various Statistical Alignment Models.” Computational Linguistics29 (1): 19–52. doi:  10.1162/089120103321337421
    https://doi.org/10.1162/089120103321337421 [Google Scholar]
  36. Onyshkevych, Boyan
    2014Low Resource Languages for Emergent Incidents (LORELEI). AccessedMarch 3, 2019. https://www.darpa.mil/program/low-resource-languages-for-emergent-incidents
    [Google Scholar]
  37. Patel, Sindur, Nirav Bhatt, Chandni Shah, and Rutvika Nanecha
    2017 “Multilingual Microblog Summarization.” InProceedings of the First International Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2017), 116–121. Aberdeen, UK: CEUR. AccessedJuly 7, 2019. ceur-ws.org/Vol-1832/
    [Google Scholar]
  38. Plitt, Mirko, and François Masselot
    2010 “A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context.” The Prague Bulletin of Mathematical Linguistics93: 7–16. doi:  10.2478/v10108‑010‑0010‑x
    https://doi.org/10.2478/v10108-010-0010-x [Google Scholar]
  39. Santos-Hernández, Jenniffer, and Betty Hearn Morrow
    2013 “Language and Literacy.” InSocial Vulnerability to Disasters, 2nd ed., edited byDeborah S. K. Thomas, Brenda D. Phillips, William E. Lovekamp, and Alice Fothergill, 265–280. Boca Raton, FL: CRC Press.
    [Google Scholar]
  40. Seki, Kaoruko
    (ed) 2008Civil-Military Guidelines & Reference for Complex Emergencies. New York, Geneva: United Nations.
    [Google Scholar]
  41. Sellnow, Timothy L., and Matthew W. Seeger
    2013Theorizing Crisis Communication. Malden, Mass: Wiley-Blackwell.
    [Google Scholar]
  42. Shackleton, Jamie
    2018 “Preparedness in Diverse Communities: Citizen Translation for Community Engagement.” InProceedings of the Information Systems for Crisis Response and Management Asia Pacific 2018 Conference, 400–406. Wellington, New Zealand: Massey University. AccessedJuly 7, 2019. ndhadeliver.natlib.govt.nz/delivery/DeliveryManagerServlet?dps_pid=IE37914290
    [Google Scholar]
  43. Sphere Project
    Sphere Project 2013Humanitarian Charter and Minimum Standards in Humanitarian Response, 3rd ed.Geneva: The Sphere Project.
    [Google Scholar]
  44. Stolcke, Andreas
    2002 “SRILM – An Extensible Language Modeling Toolkit.” InProceedings of the 7th International Conference on Spoken Language Processing (ICSLP 2002-INTERSPEECH 2002), edited byJohn H. L. Hansen and Bryan L. Pellom, 901–904. Denver, Colorado: International Speech Communication Association.
    [Google Scholar]
  45. TAUS
    TAUS 2013Adequacy/Fluency Guidelines. AccessedMarch 3, 2019. https://www.taus.net/academy/best-practices/evaluate-best-practices/adequacy-fluency-guidelines
    [Google Scholar]
  46. Teixeira, Carlos S. C.
    2014 “Perceived vs. Measured Performance in the Post-Editing of Suggestions from Machine Translation and Translation Memories.” InProceedings of the 11th Conference of the Association for Machine Translation in the Americas: Workshop on Post-Editing Technology and Practice (WPTP3), edited bySharon O’Brien, Michel Simard, and Lucia Specia, 450–59. Vancouver: AMTA.
    [Google Scholar]
  47. TWB (Translators without Borders)
    TWB (Translators without Borders) 2016Translators without Borders Develops the World’s First Crisis-Specific Machine Translation System for Kurdish Languages. AccessedJune 25, 2019. https://translatorswithoutborders.org/translators-without-borders-develops-worlds-first-crisis-specific-machine-translation-system-kurdish-languages/
    [Google Scholar]
  48. TWB (Translators without Borders)
    TWB (Translators without Borders) 2019Becoming a TWB Partner. AccessedMarch 3, 2019. https://translatorswithoutborders.org/partners/Eligibility/
    [Google Scholar]
  49. Waugh, William L., and Kathleen J. Tierney
    2007Emergency Management: Principles and Practice for Local Government. Washington, D.C.: ICMA Press.
    [Google Scholar]
  50. Ziemski, Michał, Junczys-Dowmunt, Marcin, Pouliquen, Bruno
    2016 “The United Nations Parallel Corpus v1.0.” InProceedings of the 2016 International Conference on Language Resources and Evaluation (LREC), edited byNicoletta Calzolari , 1–5. Portoroz, Slovenia: European Language Resources Association (ELRA).
    [Google Scholar]
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