Advertisement

Machine Translation and Self-post-editing for Academic Writing Support: Quality Explorations

  • Sharon O’Brien
  • Michel Simard
  • Marie-Josée Goulet
Chapter
Part of the Machine Translation: Technologies and Applications book series (MATRA, volume 1)

Abstract

Scholars who need to publish in English and who have English as a Foreign Language might consider and already be deploying free online MT engines to aid their writing processes. This raises the obvious question of whether MT is actually a useful aid for academic writing and what impact it might have on the quality of the written product. The work described in this chapter attempts to address these two broad questions. After a brief introduction, Sect. 2 reviews literature on three topics: English as a lingua franca in academic writing and the consequences this might have for individual authors and for academic disciplines, second-language writing, and the use of MT as a second-language writing aid. In Sect. 3, the methodology is presented. As will be detailed, the experiment involved ten participants, who were asked to write an abstract in their field of expertise. One half of the text was written in English, while the other half was written in their L1 and then machine-translated into English. Section 4 describes the results: subjective feedback of the participants acquired through a post-task survey, revision activity of a professional reviser, number and types of errors identified by a grammar-checking tool. The results suggest that MT and self-post-editing did not impact negatively on the text produced. However, the participants were divided in their opinions about which task was easier and whether they would consider using MT again for academic writing support. In Sect. 5, we offer a discussion on those results and provide future research ideas.

Keywords

Translation quality assessment Principles to practice English as a foreign language Machine translation Post-editing Second-language writing support Self-post-editing Academic writing 

Notes

Acknowledgments

This project was partly funded by the ADAPT Centre for Digital Content Technology, which is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

References

  1. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:14090473Google Scholar
  2. Benfield JR, Feak CB (2006) How authors can cope with the burden of English as an international language. Chest 129(6):1728–1730CrossRefGoogle Scholar
  3. Bennett K (2013) English as a lingua franca in academia. Interpret Trans Train 7(2):169–193CrossRefGoogle Scholar
  4. Bennett K (2014a) The political and economic infrastructure of academic practice: the ‘semi-periphery’ as a category for social and linguistic analysis. In: Bennett K (ed) The semi-periphery of academic writing: discourses, communities and practices. Palgrave Macmillan, London, pp 1–12CrossRefGoogle Scholar
  5. Bennett K (2014b) Conclusion: combating the centripetal pull in academic writing. In: Bennett K (ed) The semi-periphery of academic writing: discourses, communities and practices. Palgrave Macmillan, London, pp 240–246CrossRefGoogle Scholar
  6. Bennett K (2015) Towards an epistemological monoculture: mechanisms of epistemicide in European research publication. In: Plo R, Pérez-Llantada C (eds) English as an academic and research language (English in Europe vol 2). De Gruyter Mouton, Berlin, pp 9–35Google Scholar
  7. Breuer EO (2015) First language versus foreign language: Fluency, errors and revision processes in foreign language academic writing. Peter Lang Edition, Frankfurt am MainCrossRefGoogle Scholar
  8. Carl M, Gutermuth S, Hansen-Schirra S (2015) Post-editing machine translation: efficiency, strategies, and revision processes in professional translation settings. In: Ferreira A, Schwieter JW (eds) Psycholinguistic and cognitive inquiries into translation and interpreting. John Benjamins, Amsterdam, pp 145–174Google Scholar
  9. Cowan R, Choo J, Lee GS (2014) ICALL for improving Korean L2 writers’ ability to edit grammatical errors. Lang Learn Technol 18(3):193–207Google Scholar
  10. Doherty S (2012) Investigating the effects of controlled language on the reading and comprehension of machine translated texts: a mixed-methods approach. Dissertation, Dublin City UniversityGoogle Scholar
  11. Flowerdew J (2001) Attitudes of journal editors to non-native speaker contributions. TESOL Q 35(1):121–150CrossRefGoogle Scholar
  12. Foster G, Isabelle P, Plamondon P (1997) Target-text mediated interactive machine translation. Mach Trans 12(1–2):175–194CrossRefGoogle Scholar
  13. Foster G, Langlais P, Lapalme G (2002) User-friendly text prediction for translators. In: Proceedings of the 2002 conference on empirical methods in natural language processing, Philadelphia, July 2002, pp 148–155Google Scholar
  14. Garcia I (2010) Is machine translation ready yet? Target 22(1):7–21CrossRefGoogle Scholar
  15. Garcia I (2011) Translating by post-editing: is it the way forward? Mach Trans 25:217–237CrossRefGoogle Scholar
  16. Garcia I, Pena MI (2011) Machine translation-assisted language learning: writing for beginners Computer. Assist Lang Learn 24(5):471–487CrossRefGoogle Scholar
  17. Gaspari F, Toral A, Naskar SK, Groves D, Way A (2014) Perception vs reality: measuring machine translation post-editing productivity. In: Proceedings of the Third workshop on Post-Editing Technology and Practice (WPTP3), Conference for the Association for Machine Translation in the Americas (AMTA), Vancouver, pp 60–72Google Scholar
  18. González-Rubio J, Ortiz-Martínez D, Casacuberta F (2010) Balancing user effort and translation error in interactive machine translation via confidence measures. In: Proceedings of the ACL 2010 conference short papers, Uppsala, Sweden, pp 173–177Google Scholar
  19. González-Rubio J, Ortiz-Martínez D, Benedí JM, Casacuberta F (2013) Interactive machine translation using hierarchical translation models. In: Proceedings of the conference on empirical methods in natural language processing, Seattle, Washington, DC, pp 244–254Google Scholar
  20. Goulet MJ, Simard M, Parra Escartín C, O’Brien S (2017) La traduction automatique comme outil d’aide à la redaction scientifique en Anglais language seconde: résultats d’une étude exploratoire sur la qualité linguistique. ASp – La revue de la Groupe d’Étude et de Recherche en Anglais de Spécialité 72:5–28Google Scholar
  21. Green S, Heer J, Manning CD (2013) The efficacy of human post-editing for language translation. In: Proceedings of the SIGCHI conference on human factors in computing systems, Paris, France, pp 439–448Google Scholar
  22. Guerberof A (2012) Productivity and quality in the post-editing of output from translation memories and machine translation. Dissertation, Universitat Rovira i VirgiliGoogle Scholar
  23. Hanauer DI, Englander K (2011) Quantifying the burden of writing research articles in a second language: data from Mexican scientists. Writ Commun 28(4):403–416CrossRefGoogle Scholar
  24. Hardmeier C, Nakov P, Stymne S, Tiedemann J, Versley Y, Cettolo M (2015) Pronoun-focused MT and cross-lingual pronoun prediction: findings of the 2015 DiscoMT shared task on pronoun translation. In: Second workshop on discourse in Machine Translation (DiscoMT), Lisbon, pp 1–16Google Scholar
  25. Hu C, Bederson BB, Resnik P, Kronrod Y (2011) MonoTrans2: a new human computation system to support monolingual translation. In: Proceedings of the SIG-CHI conference on human factors in computing systems, Vancouver, pp 1133–1136Google Scholar
  26. Langlais P, Foster G, Lapalme G (2000) TransType: a computer-aided translation typing system. In: Proceedings of the NAACL-ANLP workshop on embedded machine translation systems, Seattle, Washington, DC, pp 46–51Google Scholar
  27. Li J, Luong MT, Jurafsky D (2015) A hierarchical neural autoencoder for paragraphs and documents. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing, Beijing, pp 1106–1115Google Scholar
  28. Lillis T, Curry MJ (2010) Academic writing in a global context: the politics and practices of publishing in English. Routledge, LondonGoogle Scholar
  29. Lin H (2015) A meta-synthesis of empirical research on the effectiveness of computer-mediated communication (CMC) in SLA. Lang Learn Technol 19(2):85–117Google Scholar
  30. López-Navarro I, Moreno AI, Quintanilla MÁ, Rey-Rocha J (2015) Why do I publish research articles in English instead of my own language? Differences in Spanish researchers’ motivations across scientific domains. Scientometrics 103:939–976CrossRefGoogle Scholar
  31. Marcu D, Carlson L, Watanabe M (2000) The automatic translation of discourse structures. In: Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference, Seattle, Washington, DC, pp 9–17Google Scholar
  32. Mitchell L, O’Brien S, Roturier J (2014) Quality evaluation in community post-editing. Mach Trans 28(3–4):237–262CrossRefGoogle Scholar
  33. Niño A (2008) Evaluating the use of machine translation post-editing in the foreign language class. Comput Assist Lang Learn 21(1):29–49CrossRefGoogle Scholar
  34. Nukoolkit C, Chansripiboon P, Mongkolnam P, Todd RW (2011) Text cohesion visualizer. In: The 6th international conference on Computer Science and Education (ICCSE 2011), Singapore, pp 205–209Google Scholar
  35. O’Brien S, Simard M (eds) (2014) Machine Translation, special issue on post-editing. 28:159–329Google Scholar
  36. O’Brien S, Simard M (eds) (2015) Proceedings of the fourth workshop on Post-Editing Technology and Practice (WPTP4), Workshop held at the MT Summit 2015, MiamiGoogle Scholar
  37. O’Brien S, Simard M, Specia L (eds) (2012) Proceedings of the first workshop on Post-Editing Technology and Practice (WPTP1), Workshop held at the Association for Machine Translation in the Americas conference (AMTA), San DiegoGoogle Scholar
  38. O’Brien S, Simard M, Specia L (eds) (2013) Proceedings of the second workshop on Post-Editing Technology and Practice (WPTP2), Workshop held at the European Association for Machine Translation conference (EAMT), NiceGoogle Scholar
  39. O’Brien S, Simard M, Specia L (eds) (2014a) Proceedings of the third workshop on Post-Editing Technology and Practice (WPTP3), Workshop held at the conference for the Association for Machine Translation in the Americas (AMTA), VancouverGoogle Scholar
  40. O’Brien S, Balling LW, Carl M, Simard M, Specia L (eds) (2014b) Post-editing of machine translation: Processes and applications. Cambridge Scholars Publishing, Newcastle-Upon-TyneGoogle Scholar
  41. O’Neill EM (2012) The effect of online translators on L2 writing in French. Dissertation, University of Illinois at Urbana-ChampaignGoogle Scholar
  42. Omar N, Razali NAM, Darus S (2009) Automated grammar checking of tenses for ESL writing. Lecture notes in computer science. Springer, Heidelberg, pp 475–482Google Scholar
  43. Parra Escartín C, O’Brien S, Goulet MJ (2017) Machine translation as an academic writing aid for medical practitioners. In: Proceedings of the Machine Translation Summit XVI, Nagoya, pp 254–267Google Scholar
  44. Pouget-Abadie J, Bahdanau D, van Merriënboer B, Cho K, Bengio Y (2014) Overcoming the curse of sentence length for neural machine translation using automatic segmentation. arXiv preprint arXiv:14091257Google Scholar
  45. Swales J (2004) Research genres: explorations and applications. Cambridge University Press, New YorkCrossRefGoogle Scholar
  46. Tatsumi M, Aikawa T, Yamamoto K, Isahara H (2012) How good is crowd post-editing? Its potential and limitations. In: Proceedings of the first workshop on Post-Editing Technology and Practice (WPTP3), Conference for the Association for Machine Translation in the Americas (AMTA), San Diego, pp 69–77Google Scholar
  47. Tu M, Zhou Y, Zong C (2013) A novel translation framework based on rhetorical structure theory. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics, Sofia, Bulgaria, pp 370–374Google Scholar
  48. Van Waes L, Leijten M (2015) Fluency in writing: a multidimensional perspective on writing fluency applied to L1 and L2. Comput Compos 38:79–95CrossRefGoogle Scholar
  49. Wagner RA, Fischer MJ (1974) The string-to-string correction problem. J ACM 21(1):168–173MathSciNetCrossRefGoogle Scholar
  50. Willey I, Tanimoto K (2015) “We’re drifting into strange territory here”: what think-aloud protocols reveal about convenience editing. J Second Lang Writ 27:63–83CrossRefGoogle Scholar
  51. Zakaria TNT, Aziz MJA, Rizan TN, Maasum TN (2010) Transformation of L2 writers to correct English: the need for a computer-assisted writing tool. In: Proceedings of the international symposium on Information Technology – System Development and Application and Knowledge Society, Kuala Lumpur, Malaysia, pp 1508–1513Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sharon O’Brien
    • 1
  • Michel Simard
    • 2
  • Marie-Josée Goulet
    • 3
  1. 1.ADAPT Centre/School of Applied Language and Intercultural StudiesDublin City UniversityDublinIreland
  2. 2.National Research CouncilOttawaCanada
  3. 3.Université du Québec en OutaouaisGatineauCanada

Personalised recommendations