Comparison of crowdsourcing translation with Machine Translation

In this paper we examine the model of crowdsourcing for translation and compare it with Machine Translation (MT). The large volume of material to be translated, the translation of this material into many languages combined with tight deadlines lead enterprises today to follow either crowdsourcing and/or MT. Crowdsourcing translation shares many characteristics with MT, as both can cope with high volume, perform at high speed, and reduce the translation cost. MT is an older technology, whereas crowdsourcing is a new phenomenon gaining much ground over time, mainly through Web 2.0. Examples and challenges of both models will be discussed and the paper is closed with future prospects regarding the combination of crowdsourcing and MT, so that they are not regarded as opponents. These prospects are partially based on the results of a survey we conducted. Based on our background, experience, and research, this paper covers aspects both from the point of view of translation studies and computational linguistics applications as well as of information sciences, and particularly the development of the Web regarding user-generated content.

[1]  John Cocke,et al.  A Statistical Approach to Language Translation , 1988, COLING.

[2]  Declan Groves.  Bringing Humans into the Loop: Localization with Machine Translation at Trasl´ an , 2008 .

[3]  Ralph Weischedel,et al.  A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION , 2005 .

[4]  Bernardo A. Huberman.  Crowdsourcing and Attention , 2008, Computer.

[5]  Sharon O'Brien,et al.  Quality and Machine Translation: A realistic objective? , 2009 .

[6]  Luis Pérez González.  FANSUBBING ANIME: INSIGHTS INTO THE ‘BUTTERFLY EFFECT’ OF GLOBALISATION ON AUDIOVISUAL TRANSLATION , 2007 .

[7]  MARTIN KAY.  The Proper Place of Men and Machines in Language Translation , 2004, Machine Translation.

[8]  Will Jordan.  From Rule-Breaking to ROM-Hacking: Theorizing the Computer Game-as-Commodity , 2007, DiGRA Conference.

[9]  Makoto Nagao.  Language engineering: the real bottle neck of natural language processing , 1988, COLING 1988.

[10]  Gary Geisler,et al.  Crowdsourcing the indexing of film and television media , 2010, ASIST.

[11]  Daniela Damian,et al.  Does distance still matter , 2008 .

[12]  John R. Pierce,et al.  Language and Machines: Computers in Translation and Linguistics , 1966 .

[13]  Alon Lavie,et al.  METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.

[14]  Rose Holley,et al.  Crowdsourcing: How and Why Should Libraries Do It? , 2010, D Lib Mag..

[15]  Katherine Del Giudice.  Crowdsourcing credibility: The impact of audience feedback on Web page credibility , 2010, ASIST.

[16]  Caroline Haythornthwaite,et al.  Crowds and communities: Light and heavyweight models of peer production , 2009 .

[17]  Bogdan Babych,et al.  Extending the BLEU MT Evaluation Method with Frequency Weightings , 2004, ACL.

[18]  Aniket Kittur,et al.  Harnessing the wisdom of crowds in wikipedia: quality through coordination , 2008, CSCW.

[19]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[20]  Minako O'Hagan,et al.  Evolution of User-generated Translation: Fansubs, Translation Hacking and Crowdsourcing , 2009 .

[21]  Mark A. Serva,et al.  Understanding member motivation for contributing to different types of virtual communities: a proposed framework , 2007, SIGMIS CPR '07.

[22]  Philipp Koehn,et al.  A Web-Based Interactive Computer Aided Translation Tool , 2009, ACL.

[23]  George R. Doddington,et al.  Automatic Evaluation of Machine Translation Quality Using N-gram Co-Occurrence Statistics , 2002 .

[24]  W. J. Hutchins.  Machine Translation: Past, Present, Future , 1986 .

[25]  Leah Hoffmann,et al.  Crowd control , 2009, CACM.

[26]  Stacey Kuznetsov,et al.  Motivations of contributors to Wikipedia , 2006, CSOC.

[27]  Sharon O'Brien,et al.  Methodologies for Measuring the Correlations between Post-Editing Effort and Machine Translatability , 2005, Machine Translation.

[28]  Harold L. Somers,et al.  Round-trip Translation: What Is It Good For? , 2005, ALTA.

[29]  J. E. Holmstrom.  Report on interlingual scientific and technical dictionaries , 1951 .

[30]  Alon Lavie,et al.  METEOR: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments , 2007, WMT@ACL.

[31]  Vassilis Kostakos,et al.  Is the Crowd's Wisdom Biased? A Quantitative Analysis of Three Online Communities , 2009, 2009 International Conference on Computational Science and Engineering.

[32]  John Hutchins,et al.  Petr Petrovich Troyanskii (1894–1950): A Forgotten Pioneer of Mechanical Translation , 2000, Machine Translation.

[33]  J. Albors,et al.  New learning network paradigms: Communities of objectives, crowdsourcing, wikis and open source , 2008 .