Can Real-Time Machine Translation Overcome Language Barriers in Distributed Requirements Engineering?

In global software projects work takes place over long distances, meaning that communication will often involve distant cultures with different languages and communication styles that, in turn, exacerbate communication problems. However, being aware of cultural distance is not sufficient to overcome many of the barriers that language differences bring in the way of global project success. In this paper, we investigate the adoption of machine translation (MT) services in synchronous text-based chat in order to overcome any language barrier existing among groups of stakeholders who are remotely negotiating software requirements. We report our findings from a simulated study that compares the efficiency and the effectiveness of two MT services, Google Translate and apertium-service, in translating the messages exchanged during four distributed requirements engineering workshops. The results show that (a) Google Translate produces significantly more adequate translations than Apertium from English to Italian; (b) both services can be used in text-based chat without disrupting real-time interaction.

[1]  R. Zacharski,et al.  User choice as an evaluation metric for web translation in cross language instant messaging applications , 2009, MTSUMMIT.

[2]  James H. Martin,et al.  Speech and Language Processing, 2nd Edition , 2008 .

[3]  Erran Carmel,et al.  Tactical Approaches for Alleviating Distance in Global Software Development , 2001, IEEE Softw..

[4]  Allen E. Milewski,et al.  Guidelines for effective eridging in global software engineering , 2008 .

[5]  Pär J. Ågerfalk,et al.  Global Software Development Challenges: A Case Study on Temporal, Geographical and Socio-Cultural Distance , 2006, 2006 IEEE International Conference on Global Software Engineering (ICGSE'06).

[6]  Doug Arnold,et al.  Machine Translation: An Introductory Guide , 1994 .

[7]  Allen E. Milewski,et al.  Guidelines for effective eridging in global software engineering , 2008, Softw. Process. Improv. Pract..

[8]  Ruslan Mitkov,et al.  The Oxford handbook of computational linguistics , 2003 .

[9]  Mikel L. Forcada,et al.  An Open-Source Shallow-Transfer Machine Translation Toolbox: Consequences of Its Release and Availability , 2005, MTSUMMIT.

[10]  Hermann Ney,et al.  The Alignment Template Approach to Statistical Machine Translation , 2004, CL.

[11]  Linda Dailey Paulson.  Translation Technology Tries to Hurdle the Language Barrier , 2001, Computer.

[12]  Judith S. Olson,et al.  Culture Surprises in Remote Software Development Teams , 2003, ACM Queue.

[13]  Toru Ishida,et al.  Effects of machine translation on collaborative work , 2006, CSCW '06.

[14]  Srinivas Bangalore,et al.  Bootstrapping Bilingual Data using Consensus Translation for a Multilingual Instant Messaging System , 2002, COLING.

[15]  Daniela E. Damian,et al.  On the Need for Mixed Media in Distributed Requirements Negotiations , 2008, IEEE Transactions on Software Engineering.

[16]  Filippo Lanubile,et al.  Using frameworks to develop a distributed conferencing system: an experience report , 2009 .

[17]  R. Garland.  The Mid-Point on a Rating Scale: Is it Desirable? , 1991 .

[18]  Douglas G. Altman,et al.  Practical statistics for medical research , 1990 .

[19]  Philippe Kruchten,et al.  Analyzing intercultural factors affecting global software development - a position paper , 2004, ICSE 2004.

[20]  Daniela E. Damian,et al.  An Empirical Investigation on Text-Based Communication in Distributed Requirements Workshops , 2007, International Conference on Global Software Engineering (ICGSE 2007).

[21]  Rini van Solingen,et al.  Tool Support for Distributed Software Engineering , 2006, 2006 IEEE International Conference on Global Software Engineering (ICGSE'06).

[22]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[23]  R. Mitkov.  The Oxford Handbook of Computational Linguistics (Oxford Handbooks) , 2003 .

[24]  Robert E. Frederking,et al.  WebDIPLOMAT: A Web-Based Interactive Machine Translation System , 2000, COLING.

[25]  Yvonne Hsieh,et al.  Culture and Shared Understanding in Distributed Requirements Engineering , 2006, 2006 IEEE International Conference on Global Software Engineering (ICGSE'06).

[26]  R. Johns,et al.  One Size Doesn’t Fit All: Selecting Response Scales For Attitude Items , 2005 .

[27]  Hideaki Kuzuoka,et al.  Difficulties in establishing common ground in multiparty groups using machine translation , 2009, CHI.

[28]  B. Everitt,et al.  Statistical methods for rates and proportions , 1973 .

[29]  W. Grove.  Statistical Methods for Rates and Proportions, 2nd ed , 1981 .

[30]  Doug Arnold.  8. Why translation is difficult for computers , 2003 .

[31]  James D. Herbsleb,et al.  Guest Editors' Introduction: Global Software Development , 2001, IEEE Softw..

[32]  Pasquale Minervini,et al.  Apertium goes SOA: an efficient and scalable service based on the Apertium rule-based machine translation platform , 2009 .

[33]  James D. Herbsleb,et al.  Global Software Engineering: The Future of Socio-technical Coordination , 2007, Future of Software Engineering (FOSE '07).