Humans in the Loop of Localization Processes

Chapter
Part of the Cognitive Technologies book series (COGTECH)

Abstract

The Language Grid is a service-oriented infrastructure for language services. In the Language Grid, machine translation services play important roles in supporting multilingual activities for communities. Although the effectiveness of using machine translation services for multilingual communication has been shown in previous reports, the gap between human translators and machine translators remains huge especially in the domain of localization processes that require high translation quality. In this chapter, we aim at improving localization processes by introducing humans into the loop to utilize machine translation services. We try to compare several different types of localization processes (i.e., absolute machine translation processes, absolute human translation processes and processes by human and machine translation services) in the dimensions of translation quality and translation cost. The experiment results show that monolinguals can help improve the translation quality of machine translators with the aid of community dictionary services, and that collaboration of human and machine translation services make it possible to reduce the cost compared with absolute human translations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal R, Verma K, Miller J, Milnor W (2004) Constraint driven web service composition in METEOR-S. 2004 IEEE International Conference on Services Computing (SCC 2004):23–30Google Scholar
  2. Alves A, Arkin A, Askary S, Barreto C, Bloch B, Curbera F, Ford M, Goland Y, Guızar A, Kartha N, et al (2007) Web services business process execution language version 2.0. OASIS Standard 11Google Scholar
  3. Cardoso J, Sheth AP, Miller JA, Arnold J, Kochut K (2004) Quality of service for workflows and web service processes. Journal of Web Semantics 1(3):281–308CrossRefGoogle Scholar
  4. Hu C. (2009) Collaborative translation by monolingual users. 27th international conference extended abstracts on Human factors in computing systems: 3105–3108Google Scholar
  5. Inaba R, Murakami Y, Nadamoto A, Ishida T (2007) Multilingual communication support using the Language Grid. Intercultural Collaboration. Lecture Notes in Computer Science 4568, Springer, Berlin: 118–132Google Scholar
  6. Ishida T (2006) Language Grid: an infrastructure for intercultural collaboration. IEEE/IPSJ Symposium on Applications and the Internet (SAINT-06):96–100Google Scholar
  7. Ishida T (2008) Service-oriented collective intelligence for intercultural collaboration. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’08) 1:4–8Google Scholar
  8. Ishida T (2010) Intercultural collaboration using machine translation. IEEE Internet Computing, January/February 2010:26–28Google Scholar
  9. Kloppmann M, Koenig D, Leymann F, Pfau G, Rickayzen A, von Riegen C, Schmidt P, Trickovic I (2005) WS-BPEL extension for people–BPEL4People. Joint white paper, IBM and SAP.Google Scholar
  10. Menascé DA (2002) QoS issues in web services. IEEE Internet Computing 6(6):72–75CrossRefGoogle Scholar
  11. Mendling J, Ploesser K, Strembeck M (2008) Specifying separation of duty constraints in BPEL4People processes. 11th International Conference on Business Information Systems (Bis 2008):273–284Google Scholar
  12. Morita D, Ishida T (2009) Collaborative translation by monolinguals with machine translators. 13th International Conference on Intelligent User Interfaces:361–365Google Scholar
  13. Murakami Y, Ishida T (2008) A layered language service architecture for intercultural collaboration. 6th International Conference on Creating, Connecting and Collaborating through Computing (c5 2008):3–9Google Scholar
  14. Russell N, Aalst WM. (2008) Work distribution and resource management in BPEL4People: capabilities and opportunities. 20th International Conference on Advanced Information Systems Engineering, Lecture Notes in Computer Science 5074, Springer, Berlin, Heidelberg:94–108Google Scholar
  15. White J, O’Connell T, O’Mara F (1994) The ARPA MT evaluation methodologies: evolution, lessons, and future approaches. 1st Conference of the Association for Machine Translation in the Americas:193–205Google Scholar
  16. Yamashita N Ishida T (2006) Effects of machine translation on collaborative work. 20th Conference on Computer Supported Cooperative Work:515–524Google Scholar
  17. Yamashita N, Inaba R, Kuzuoka H, Ishida T (2009) Difficulties in establishing common ground in multiparty groups using machine translation. 27th International Conference on Human Factors in Computing Systems:679–688Google Scholar
  18. Zeng L, Benatallah B, Ngu AHH, Dumas M, Kalagnanam J, Chang H (2004) QoS-aware middleware for web services composition. IEEE Transactions on Software Engineering, 30(5):311–327CrossRefGoogle Scholar
  19. Zhao X, Qiu Z, Cai C, Yang H (2008) A formal model of human workflow. 2008 IEEE International Conference on Web Services:195–202Google Scholar
  20. Zur Muehlen M (2004) Organizational management in workflow applications–issues and perspectives. Information Technology and Management 5(3–4):271–291CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.National Institute of Information and Communications Technology (NICT)KyotoJapan

Personalised recommendations