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Article published In:
Translation Spaces
Vol. 8:2 (2019) ► pp.300–333

More than tweets

A critical reflection on developing and testing crisis machine translation technology

ORCID logo | Dublin City University
ORCID logo | Dublin City University
 | Translators without Borders
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.
Keywords: crisis translation, crisis, machine translation (MT), post-editing, evaluation, training, citizen translators, data sets
Article outline
  • Introduction
  • Crisis translation: Research foci and blind spots
  • Crisis machine translation: A feasibility study
  • Findings from the study
    • Human translation
    • Post-edit
    • Machine Translation
  • Discussion
  • Conclusions
  • Acknowledgements
  • Notes
  • Abbreviations
  • References
Published online: 5 November 2019
DOI logo
https://doi.org/10.1075/ts.19018.cad
References (50)
References
Al-Dahash, Hajer, Menaha Thayaparan, and Udayangani Kulatunga. 2016. “Understanding the Terminologies: Disaster, Crisis and Emergency.” In Proceedings of the 32nd Association of Researchers in Construction Management (ARCOM), 1191–1200. Manchester, UK: Association of Researchers in Construction Management.Google Scholar
Alexander, David. 2002. Principles of Emergency Planning and Management. Oxford: Oxford University Press.Google Scholar
Ansari, Aimee, and Rebecca Petras. 2018. Gamayun: The Language Equality Initiative. Accessed March 3, 2019. [URL]
Cadwell, Patrick. 2016. “A Place for Translation Technologies in Disaster Settings: The Case of the 2011 Great East Japan Earthquake.” In Conflict and Communication: A Changing Asia in a Globalising World, edited by Minako O’Hagan, and Qi Zhang, 169–194. New York: Nova Science Publishers.Google Scholar
Cadwell, Patrick, and Sharon O’Brien. 2016. “Language, Culture, and Translation in Disaster ICT: An Ecosystemic Model of Understanding.” Perspectives 24 (4): 557–575. DOI logoGoogle Scholar
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.” In Proceedings of the 17th Conference of the European Association for Machine Translation, edited by Marko Tadić, Philipp Koehn, Johann Roturier, and Andy Way, 183–190. Dubrovnik: EAMT.Google Scholar
Castilho, Sheila, Stephen Doherty, Federico Gaspari, and Joss Moorkens. 2018. “Approaches to Human and Machine Translation Quality Assessment.” In Translation Quality Assessment: From Principles to Practice, edited by Joss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 9–38. Cham: Switzerland. DOI logoGoogle Scholar
Chu, Chenhui, and Rui Wang. 2018. “A Survey of Domain Adaptation for Neural Machine Translation.” In Proceedings of the 27th International Conference on Computational Linguistics, edited by Emily M. Bender, Leon Derczynski, and Pierre Isabelle, 1304–1319. Santa Fe, New Mexico: Association for Computational Linguistics, [URL]
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.” In Proceedings of the Third Conference on Machine Translation, 224–231. Brussels, Belgium: Association for Computational Linguistics, [URL]
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 Interaction 30 (1): 40–51. DOI logoGoogle Scholar
Federici, Federico M. (ed). 2016. Mediating Emergencies and Conflicts. Houndmills: Palgrave Macmillan. DOI logoGoogle Scholar
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 Spaces 7 (1): 23–43. DOI logoGoogle Scholar
Federici, Federico M., Brian J. Gerber, Sharon O’Brien, and Patrick Cadwell. 2019. The 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
Fischer, Henry W. 2008. Response to Disaster: Fact versus Fiction and Its Perpetuation: The Sociology of Disaster. Lanham, MD: University Press of America.Google Scholar
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 Trainer 8 (2): 257–275. DOI logoGoogle Scholar
Gaspari, Federico, Antonio Toral, Sudip Kumar Naskar, Declan Groves, and Andy Way. 2014. “Perception vs Reality: Measuring Machine Translation Post-Editing Productivity.” In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Workshop on Post-Editing Technology and Practice (WPTP3), edited by Sharon O’Brien, Michel Simard, and Lucia Specia, 60–72. Vancouver: AMTA.Google Scholar
Guerberof Arenas, Ana. 2009. “Productivity and Quality in the Post-editing of Outputs from Translation Memories and Machine Translation.” Localisation Focus 7 (1): 11–21.Google Scholar
Haddow, George D., Jane A. Bullock, and Damon P. Coppola. 2011. Introduction to Emergency Management. Burlington, MA: Butterworth Heinemann.Google Scholar
Harvard Humanitarian Initiative. 2011. Disaster 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
IDMC (Internal Displacement Monitoring Centre). 2018. Global Report on Internal Displacement 2018. Accessed March 3, 2019. [URL]
Karakanta, Alina, Jon Dehdari, Josef van, Genabith J. 2018. “Neural Machine Translation for Low-Resource Languages without Parallel Corpora.” Machine Translation 32 (1–2), 167–189. DOI logoGoogle Scholar
Kobus, Catherine, Josep Crego, and Jean Senellart. 2017. “Domain Control for Neural Machine Translation.” In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP, 372–378. Varna, Bulgaria: Association for Computational Linguistics. DOI logoGoogle Scholar
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.” In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, 177–180. Prague, Czech Republic: Association for Computational Linguistics.Google Scholar
Koponen, Maarit. 2012. “Comparing Human Perceptions of Post-Editing Effort with Post-Editing Operations.” In Proceedings of the 7th Workshop on Statistical Machine Translation, 181–190. New York: Association for Computational Linguistics.Google Scholar
. 2015. “How to Teach Machine Translation Post-Editing? Experiences from a Post-Editing Course.” In Proceedings of the 4th Workshop on Post-Editing Technology and Practice (WPTP4), 2–15. Miami: Association for Computational Linguistics.Google Scholar
Lewis, William D. 2010. “Haitian Creole: How to Build and Ship an MT Engine from Scratch in 4 Days, 17 Hours, & 30 Minutes.” In Proceedings of the 14th Annual Conference of the European Association for Machine Translation (EAMT 2010) (no pagination). Saint-Raphaël, France: EAMT. Accessed July 7, 2019. [URL]
Lewis, William D., Robert Munro, and Stephan Vogel. 2011. “Crisis MT: Developing a Cookbook for MT in Crisis Situations.” In Proceedings of the 6th Workshop on Statistical Machine Translation, 501–511. Edinburgh, Scotland: UK Association for Computational Linguistics.Google Scholar
Liu, Chao-Hong. 2018. Workshop Proceedings of Technologies for MT of Low Resource Languages (LoResMT 2018). Accessed March 3, 2019. [URL]
Liu, Chao-Hong, Catarina Cruz Silva, Longyue Wang, and Andy Way. 2018. “Pivot Machine Translation Using Chinese as Pivot Language.” In Proceedings of the 14th China Workshop on Machine Translation, 1–12. Wuyishan, China: Springer Nature Singapore Pte Ltd. DOI logoGoogle Scholar
Mehra, Kanav, and Vibhash Chandra. 2017. “Summarizing Microblogs for Emergency Relief and Preparedness.” In Proceedings of the First International Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2017), 104–108. Aberdeen, UK: CEUR. Accessed July 7, 2019. [URL]
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 Translation 29 (3–4): 267–284. DOI logoGoogle Scholar
O’Brien, Sharon. Forthcoming. “Translation Technology and Disaster Management.” In The Routledge Handbook of Translation and Technology, edited by Minako O’Hagan. Abingdon, Oxon: Routledge. DOI logo
O’Brien, Sharon, and Patrick Cadwell. 2017. “Translation Facilitates Comprehension of Health-Related Crisis Information: Kenya as an Example.” The Journal of Specialised Translation 281: 23–51. Accessed July 7, 2019. [URL]
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 Reduction 311: 627–636. DOI logoGoogle Scholar
Och, Franz Josef, and Hermann Ney. 2003. “A Systematic Comparison of Various Statistical Alignment Models.” Computational Linguistics 29 (1): 19–52. DOI logoGoogle Scholar
Onyshkevych, Boyan. 2014. Low Resource Languages for Emergent Incidents (LORELEI). Accessed March 3, 2019. [URL]
Patel, Sindur, Nirav Bhatt, Chandni Shah, and Rutvika Nanecha. 2017. “Multilingual Microblog Summarization.” In Proceedings of the First International Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2017), 116–121. Aberdeen, UK: CEUR. Accessed July 7, 2019. [URL]
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 Linguistics 931: 7–16. DOI logoGoogle Scholar
Santos-Hernández, Jenniffer, and Betty Hearn Morrow. 2013. “Language and Literacy.” In Social Vulnerability to Disasters, 2nd ed., edited by Deborah S. K. Thomas, Brenda D. Phillips, William E. Lovekamp, and Alice Fothergill, 265–280. Boca Raton, FL: CRC Press.Google Scholar
Seki, Kaoruko. (ed). 2008. Civil-Military Guidelines & Reference for Complex Emergencies. New York, Geneva: United Nations.Google Scholar
Sellnow, Timothy L., and Matthew W. Seeger. 2013. Theorizing Crisis Communication. Malden, Mass: Wiley-Blackwell.Google Scholar
Shackleton, Jamie. 2018. “Preparedness in Diverse Communities: Citizen Translation for Community Engagement.” In Proceedings of the Information Systems for Crisis Response and Management Asia Pacific 2018 Conference, 400–406. Wellington, New Zealand: Massey University. Accessed July 7, 2019. [URL]
Sphere Project. 2013. Humanitarian Charter and Minimum Standards in Humanitarian Response, 3rd ed. Geneva: The Sphere Project.Google Scholar
Stolcke, Andreas. 2002. “SRILM – An Extensible Language Modeling Toolkit.” In Proceedings of the 7th International Conference on Spoken Language Processing (ICSLP 2002-INTERSPEECH 2002), edited by John H. L. Hansen and Bryan L. Pellom, 901–904. Denver, Colorado: International Speech Communication Association.Google Scholar
TAUS. 2013. Adequacy/Fluency Guidelines. Accessed March 3, 2019. [URL]
Teixeira, Carlos S. C. 2014. “Perceived vs. Measured Performance in the Post-Editing of Suggestions from Machine Translation and Translation Memories.” In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Workshop on Post-Editing Technology and Practice (WPTP3), edited by Sharon O’Brien, Michel Simard, and Lucia Specia, 450–59. Vancouver: AMTA.Google Scholar
TWB (Translators without Borders). 2016. Translators without Borders Develops the World’s First Crisis-Specific Machine Translation System for Kurdish Languages. Accessed June 25, 2019. [URL]
. 2019. Becoming a TWB Partner. Accessed March 3, 2019. [URL]
Waugh, William L., and Kathleen J. Tierney. 2007. Emergency Management: Principles and Practice for Local Government. Washington, D.C.: ICMA Press.Google Scholar
Ziemski, Michał, Junczys-Dowmunt, Marcin, Pouliquen, Bruno. 2016. “The United Nations Parallel Corpus v1.0.” In Proceedings of the 2016 International Conference on Language Resources and Evaluation (LREC), edited by Nicoletta Calzolari et al., 1–5. Portoroz, Slovenia: European Language Resources Association (ELRA).Google Scholar
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