LE language editing (pre/post editing)

 ED  attempts to find uses for MT as aids for beginning language learners using beginning Spanish students

 ED basically same as above

KEY:　 MT　machine translation TQ translation quality TT translation tools LE language editing ED education and MT LL linguistic landscapes and signage

MT, TQ found that GT and HT were similar, but does not mention the languages used. Waddington's model of

 MT, TQ Compares 4 MT sites for Eng-Spanish, finds Bing and GT best. Some good lit review. --JY

 LL Combines empirical data from a photographic database (=736 photos)

 TQ, TT uses Amazon’s Mechanical Turk to pay small sums to a large number of non-expert annotators.

 ED Describes use of SMT in syllabus for graduate students of translation

 LL See below 

 LL Both of the articles above discuss the adoption of food and drink multilingual signage at a university in northern Thailand as part of a local cultural revitalization process.

 LL Explores use of bilingual signs in German-English elementary school in Western Canada from nexus analysis viewpoint.

 ED

 ED 

 Both of the articles above discuss the adoption of food and drink multilingual signage at a university in northern Thailand as part of a local cultural revitalization process.

 uses Amazon’s Mechanical Turk to pay small sums to a large number of non-expert annotators.
 for judgments on a translation task. Found that non-expert judgments have a high-level of
 agreement with the existing gold-standard judgments of machine translation quality

 combines empirical data from a photographic database (=736 photos)
 with the self-reported sociolinguistic profile of 400 interviewees to explore sociolinguistic 
 patterns in 2 tourist areas in Spain.

http://theconversation.com/learning-a-language-and-translating-the-web-does-duolingo-work-10687

 found that GT and HT were similar, but does not mention the languages used. Waddington's model of 
 translation quality assessment (2001) could be useful; merits a read.

 found that GT and HT were similar, but does not mention the languages used. Waddington's model of translation quality assessment (2001) could be useful; merits a read.

田中?. (2016).

 Compares 4 MT sites for Eng-Spanish, finds Bing and GT best. Some good lit review. --JY

 Compares 4 MT sites for Eng-Spanish, finds Bing and GT best. Some good lit review.

　Compares 4 MT sites for Eng-Spanish, finds Bing and GT best. Some good lit review.

NIM *Shen?, Ethan (2010) Comparison of online machine translation tools http://www.tcworld.info/e-magazine/translation-and-localization/article/comparison-of-online-machine-translation-tools/

 Found 1)  Google Translate is widely preferred when translating long passages
  2) Microsoft Bing Translator and Yahoo Babelfish often produce better translations for phrases below 140 characters. 
 3) in general Babelfish performs well in East Asian Languages such as Chinese and Korean and Bing Translator 
 performs well in Spanish, German, and Italian.

NIM Yamada?, Masaru (2013) Who will be post-editors. Introducing Translation Studies, 10 51-64 Aug 2013 http://honyakukenkyu.sakura.ne.jp/shotai_vol10/No_10-004-Yamada.pdf

NIM Yamada?, Masaru (2011) Applying ‘machine translation plus post-editing’ to a case of English-to-Japanese translation (9) 97-114 A study of the translation process through translators’ interim products

NIM Yamada?, Masaru (2010) The effect of translation memory database for productivity Interpreting and Translation Studies (9) 159-176

NIM Yamada?, Masaru (11 Apr 2013) A pilot investigation on possibilities for novice translators to be post-editors in MT+PE settings, TAUS Executive Forum in Tokyo.

NIM Yamada?, Masaru (19 Apr 2012) MT plus post-editing in an English-to-Japanese localization context: How useful can it be, compared to translation memory, and how does it change professional translators’ production style? TAUS Executive Forum in Tokyo

NIM Yamada?, Masaru (Jun 2009) Effect of the use of TM in localization industries on the translation products and process Graduate conference at Universitat Rovira I Virgili

NIM Matsudaira?, M. (1994) A pre-editing support system for Japanese-English machine translating.

 Suggests the following preedit patterns: division of longer sentences, addition of subject, object, blocking to show 

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http://www.academia.edu/11573216/Comparison_of_Google_Online_Translation_and_Human_Translation_with_Regard_to_ Soft_vs._Hard_Science_Texts

 3) in general Babelfish performs well in East Asian Languages such as Chinese and Korean and Bing Translator 
 performs well in Spanish, German, and Italian.

Yamada, Masaru (2012) Revising text: An empirical investigation of revision and the effects of integrating a TM and MT system into the translation process http://apple-eye.com/rikkyo/YAMADA_2011.pdf

Agnihotri?, R. K., & Mc　Cormick, K. (2010). Language in the Material World: Multilinguality in Signage. International Multilingual Research Journal, 4(1), 55–81. http://doi.org/10.1080/19313150903501133

Abbasian, Gholam-Reza & Parisa Aslerasouli (2015) Comparison of Google Online Translation and Human Translation with Regard to Soft vs. Hard Science Texts, Journal of Applied Linguistics and Language Research Volume 2, Issue 3, 2015, pp. 169-184

Matsudaira?, M. (1994) A pre-editing support system for Japanese-English machine translating. Shizen gengo Shori p. 104-118.

 Developed pre-editing support system for -E machine translation using patterns and rules. 
 Suggests the following preedit patterns: division of longer sentences, addition of subect, object, blocking to show 
 structure, fulfilling meaning, and other grammar changes. Hyo 4 is good.

Jian-Yun Nie, Michel Simard, Pierre Isabelle, Richard Durand (1999) Cross-Language Information Retrieval based on Parallel Texts and Automatic Mining of Parallel Texts from the Web

Yamada, Masaru (2015) Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings Machine Translation March 2015, Volume 29, Issue 1, pp 49-67

YAMADA, Masaru (3) 63-74 2010 Conference Activities & Talks Translation training for undergraduate and graduate students: introducing MTPE for novice translators

YAMADA, Masaru chuckmy 5 Sep 2013 Who can be a post-editor: An investigation into the possibilities for college students to be post-editors in machine translation plus post-editing settings

Yamada, Masaru (11 Apr 2013) A pilot investigation on possibilities for novice translators to be post-editors in MT+PE settings, TAUS Executive Forum in Tokyo.

Yamada, Masaru (19 Apr 2012) MT plus post-editing in an English-to-Japanese localization context: How useful can it be, compared to translation memory, and how does it change professional translators’ production style? TAUS Executive Forum in Tokyo

Agnihotri?, R. K., & Mc　Cormick, K. (2010). Language in the Material World: Multilinguality in Signage. International Multilingual Research Journal, 4(1), 55–81. http://doi.org/10.1080/19313150903501133 Arenas, A. G. (2010). Exploring Machine Translation on the Web. Revista Tradumàtica, 1–6.

Austermuh?, Frank (2001) Electronic Tools for Translators. NY: Routledge.

Abbasian, Gholam-Reza & Parisa Aslerasouli (2015) Comparison of Google Online Translation and Human Translation with Regard to Soft vs. Hard Science Texts, Journal of Applied Linguistics and Language Research Volume 2, Issue 3, 2015, pp. 169-184

Matsudaira?, M. (1994) A pre-editing support system for Japanese-English machine translating. Shizen gengo Shori p. 104-118.

Yamada?, Masaru (2013) Who will be post-editors. Introducing Translation Studies, 10 51-64 Aug 2013

Agnihotri 2010? Agnihotri, R. K., & Mc　Cormick, K. (2010). Language in the Material World: Multilinguality in Signage. International Multilingual Research Journal, 4(1), 55–81. http://doi.org/10.1080/19313150903501133 Arenas 2010 Arenas, A. G. (2010). Exploring Machine Translation on the Web. Revista Tradumàtica, 1–6.

Austermuh 2001 Austermuh, Frank (2001) Electronic Tools for Translators. NY: Routledge.

. Language in the Material World: Multilinguality in Signage. International Multilingual Research Journal, 4(1), 55–81. http://doi.org/10.1080/19313150903501133 . Exploring Machine Translation on the Web. Revista Tradumàtica, 1–6. . Interpretation and misinterpretation of warning signage: Perceptions of rockfalls in a naturalistic setting. Psychology, Health & Medicine, 17(5), 522–529. http://doi.org/10.1080/13548506.2011.644247

• Austermuh, Frank (2001) Electronic Tools for Translators. NY: Routledge.

test

Austermuh, Frank Electronic Tools for Translators. NY: Routledge

 Introduces TM sites has good chapters on MT 

test

[[Agnihotri, R. K., & Mc　Cormick, K. (2010). Language in the Material World: Multilinguality in Signage. International Multilingual Research Journal, 4(1), 55–81. http://doi.org/10.1080/19313150903501133 [[Arenas, A. G. (2010). Exploring Machine Translation on the Web. Revista Tradumàtica, 1–6. [[Aucote, H. M., Miner, A., & Dahlhaus, P. (2012). Interpretation and misinterpretation of warning signage: Perceptions of rockfalls in a naturalistic setting. Psychology, Health & Medicine, 17(5), 522–529. http://doi.org/10.1080/13548506.2011.644247 [[Bauder, M., & Freytag, T. (2015). Visitor mobility in the city and the effects of travel preparation. Tourism Geographies, 6688(September), 1–19. http://doi.org/10.1080/14616688.2015.1053971 [[Bruyèl-Olmedo, A., & Juan-Garau, M. (2013). Shaping tourist LL: language display and the sociolinguistic background of an international multilingual readership. International Journal of Multilingualism, 00(00), 1–17. http://doi.org/10.1080/14790718.2013.827688

test

• Austermuh, Frank (2001) Electronic Tools for Translators. NY: Routledge. Introduces TM sites has good chapters on MT

Abbasian, Gholam-Reza & Parisa Aslerasouli (2015) Comparison of Google Online Translation and Human Translation with Regard to Soft vs. Hard Science Texts, Journal of Applied Linguistics and Language Research Volume 2, Issue 3, 2015, pp. 169-184

Matsudaira, M. (1994) A pre-editing support system for Japanese-English machine translating. Shizen gengo Shori p. 104-118. Developed pre-editing support system for -E machine translation using patterns and rules. Suggests the following preedit patterns: division of longer sentences, addition of subect, obect, blocking to show structure, fulfilling meaning, and other grammar changes. Hyo 4 is good.

Jian-Yun Nie, Michel Simard, Pierre Isabelle, Richard Durand (1999) Cross-Language Information Retrieval based on Parallel Texts and Automatic Mining of Parallel Texts from the Web

Shen, Ethan (2010) Comparison of online machine translation tools http://www.tcworld.info/e-magazine/translation-and-localization/article/comparison-of-online-machine-translation-tools/

 Found 1)  Google Translate is widely preferred when translating long passages
  2) Microsoft Bing Translator and Yahoo Babelfish often produce better translations for phrases below 140 characters. 
 3) in general Babelfish performs well in East Asian Languages such as Chinese and Korean and Bing Translator performs well in Spanish, German, and Italian.

Yamada, Masaru (2013) Who will be post-editors. Introducing Translation Studies, 10 51-64 Aug 2013

Yamada, Masaru (2015) Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings Machine Translation March 2015, Volume 29, Issue 1, pp 49-67

Yamada, Masaru (2012) Revising text: An empirical investigation of revision and the effects of integrating a TM and MT system into the translation process

YAMADA, Masaru (2011) Applying ‘machine translation plus post-editing’ to a case of English-to-Japanese translation (9) 97-114

YAMADA, Masaru (2010) The effect of translation memory database for productivity Interpreting and Translation Studies (9) 159-176

Who can be a post-editor: An investigation into the possibilities for college students to be post-editors in machine translation plus post-editing settings

Yamada, Masaru (11 Apr 2013) A pilot investigation on possibilities for novice translators to be post-editors in MT+PE settings, TAUS Executive Forum in Tokyo.

Yamada, Masaru (19 Apr 2012) MT plus post-editing in an English-to-Japanese localization context: How useful can it be, compared to translation memory, and how does it change professional translators’ production style? TAUS Executive Forum in Tokyo

Yamada, Masaru (Jun 2009) Effect of the use of TM in localization industries on the translation products and process Graduate conference at Universitat Rovira I Virgili

http://theconversation.com/learning-a-language-and-translating-the-web-does-duolingo-work-10687

Abbasian Gholam-Reza adn Parisa Aslerasouli (2015) Comparison of Google Online Translation and Human Translation with Regard to Soft vs. Hard Science Texts, Journal of Applied Linguistics and Language Research Volume 2, Issue 3, 2015, pp. 169-184 http://www.academia.edu/11573216/Comparison_of_Google_Online_Translation_and_Human_Translation_with_Regard_to_Soft_vs._Hard_Science_Texts

 There is statistically a significant difference in the quality of HT and MT (i.e. GT) in favor of HT.
 Mode of translation affects its quality but text type does not have any significant effects on translation quality.
 No statistically significant relationship exists among translation errors and translation modes

Shen, Ethan (2010) Comparison of online machine translation tools http://www.tcworld.info/e-magazine/translation-and-localization/article/comparison-of-online-machine-translation-tools/

 Found 1)  Google Translate is widely preferred when translating long passages
  2) Microsoft Bing Translator and Yahoo Babelfish often produce better translations for phrases below 140 characters. 
 3) in general Babelfish performs well in East Asian Languages such as Chinese and Korean and Bing Translator performs well in Spanish, German, and Italian.