Bringing humans into the loop: Localization with MT at Traslán

Traslán makes full use of MT during our translation workflow, where the raw output from our Machine Translation (MT) system is passed onto human translators who perform post-editing (if necessary) to arrive at the final translation. Within Traslán we have found that using MT has enabled us to increase the speed, accuracy and consistency of translation elements which allow us to process larger amounts of translation; with quicker turnaround times, which in turn has resulted in overall savings of approx. 20% so far. One of the main challenges in using MT within a commercial setting is getting human translators to adopt and make full use of the technology. Within Traslán we overcome this obstacle by working closely and intensively with our translators, getting them involved directly in the development process. Doing so enables translators in turn to train new users of the system and to communicate effectively to other translators the benefits of integrating MT into the translation pipeline.

[1]  Luis Iraola,et al.  TransType2 - A New Paradigm for Translation Automation , 2001 .

[2]  Dorothy Senez Post-editing Service for Machine Translation Users at the European Commission , 1998, TC.

[3]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[4]  Jeffrey Allen Case Study: Implementing MT for the Translation of Pre-sales Marketing and Post-sales Software Deployment Documentation at Mycom International , 2004, AMTA.

[5]  Chris Callison-Burch,et al.  Secondary benefits of feedback and user interaction in machine translation tools , 2001, MTSUMMIT.

[6]  Tomaz Erjavec,et al.  The JRC-Acquis: A Multilingual Aligned Parallel Corpus with 20+ Languages , 2006, LREC.

[7]  Joseph P. Turian,et al.  Evaluation of machine translation and its evaluation , 2003, MTSUMMIT.

[8]  Management of the machine translation environment: interaction of functions at the Pan American Health Organization , 1983, TC.

[9]  Bente Maegaard,et al.  PaTrans- A Patent Translation System , 1996, COLING.

[10]  Andy Way Translating with examples: the LFG-DOT models of translation , 2003 .

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

[12]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[13]  Andy Way,et al.  Hybrid Example-Based SMT: the Best of Both Worlds? , 2005, ParallelText@ACL.

[14]  Lucian Vlad Lita,et al.  tRuEcasIng , 2003, ACL.

[15]  John Hutchins Machine translation and human translation: in competition or in complementation? , 2001 .

[16]  Sergei Nirenburg,et al.  Two Approaches to Matching in Example-Based Machine Translation , 1993, TMI.

[17]  Joann P. Ryan The role of the translator in making an MT system Work: Perspective of a Developer , 1988 .

[18]  Kathryn B. Taylor,et al.  Machine Translation: From Real Users to Research , 2004, Lecture Notes in Computer Science.

[19]  Andy Way,et al.  Hybrid data-driven models of machine translation , 2005, Machine Translation.

[20]  Andy Way,et al.  Hybridity in MT. Experiments on the Europarl Corpus , 2006, EAMT.

[21]  Sharon O'Brien,et al.  Machine-translatability and post-editing effort: an empirical study using translog and choice network analysis , 2006 .

[22]  Andy Way,et al.  MaTrEx: machine translation using examples , 2006 .