Application of machine translation in localization into low-resourced languages

This paper evaluates the impact of machine translation on the software localization process and the daily work of professional translators when SMT is applied to low-resourced languages with rich morphology. Translation from English into six low-resourced languages (Czech, Estonian, Hungarian, Latvian, Lithuanian and Polish) from different language groups are examined. Quality, usability and applicability of SMT for professional translation were evaluated. The building of domain and project tailored SMT systems for localization purposes was evaluated in two setups. The results of the first evaluation were used to improve SMT systems and MT platform. The second evaluation analysed a more complex situation considering tag translation and its effects on the translator’s productivity.

[1]  François Masselot,et al.  A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context , 2010, Prague Bull. Math. Linguistics.

[2]  Sergei Nirenburg,et al.  The Proper Place of Men and Machines in Language Translation , 2003 .

[3]  Jörg Tiedemann,et al.  News from OPUS — A collection of multilingual parallel corpora with tools and interfaces , 2009 .

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

[5]  Raymond Flournoy,et al.  MT and Document Localization at Adobe: From Pilot to Production , 2009, MTSUMMIT.

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

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

[8]  Jörg Tiedemann,et al.  LetsMT!: Cloud-Based Platform for Do-It-Yourself Machine Translation , 2012, ACL.

[9]  Christine Bruckner,et al.  Evaluating the operational benefit of using machine translation output as translation memory input , 2001, MTSUMMIT.

[10]  Carlos Teixeira.  Knowledge of Provenance and its Effects on Translation Performance in an Integrated TM / MT Environment , 2011 .

[11]  Andreas Eisele,et al.  DGT-TM: A freely available Translation Memory in 22 languages , 2012, LREC.

[12]  Alon Lavie,et al.  Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems , 2011, WMT@EMNLP.

[13]  Barry Haddow,et al.  Improved Minimum Error Rate Training in Moses , 2009, Prague Bull. Math. Linguistics.

[14]  Martin Kay,et al.  The MIND Translation System: A Study in Man-Machine Collaboration. , 1972 .

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

[16]  Raivis Skadins,et al.  Improving SMT for Baltic Languages with Factored Models , 2010, Baltic HLT.