Yury Muravev
ABSTRACT
The study aims to analyze the use of legal tech and machine translation software in legal translation training. It presents a comparative research of various IT tools and applications in foreign language learning environments, their capabilities and limitations in higher education institutions, as well as the author's recommendations on integrating them in the ESP-teaching process. This research's primary methods are case study method, analysis of software teaching materials, descriptive statistics, and translation studies methods.
The study results may improve the output quality of machine translation systems and legal tech software development for legal translation training. The secondary goal is to find software-based teaching methods that may enhance the learning motivation of Legal English students by realistic scenarios of business simulation games. The novelty aspect is implementing adjustable frames in the tasks involving legal tech use in a classroom setting. The study results show that legal tech software may be successfully applied in the acceleration and facilitation of ESP teaching and blended learning.
- Baffy, Marta. 2017. Shifting frames to construct a legal English class. Journal of English for Academic Purposes, 25, 58--70. https://dx.doi.org/10.1016/j.jeap.2016.11.003Google Scholar
Cross Ref
- Bajcic, Martina. 2017. New insights into the semantics of legal concepts and the legal dictionary. Amsterdam: John Benjamins BV. https://doi.org/10.1075/tlrp.17Google Scholar
- Cao, Deborah. 2010. Legal translation. Translating legal language. The Routledge Handbook of Forensic Linguistics. Abingdon-on-Thames: Routledge.Google Scholar
- David, Rene. 1968. Major legal systems in the world today. John E. C. Brierley, London.Google Scholar
- Fillmore, Charles James. 1985. Frames and the semantics of understanding. Quaderni di semantica, 2, 222--254.Google Scholar
- Garzone, Giuliana, Viezzi, Maurizio. 2002. "Interpreting in the 21st century: challenges and opportunities." In Selected papers from the 1st Forli Conference on Interpreting Studies (pp. 227--229). Amsterdam: John Benjamins Publishing Company. https://doi.org/10.1075/target.16.2.18gilGoogle Scholar
- Hockly Nicky. 2007. How to Teach English with Technology London: Longman.Google Scholar
- Hu, Weifeng. 2019. On legal English translation from the perspective of legal linguistics. Review of Educational Theory. 2--6. https://doi.org/10.30564/ret.v2i3.870Google Scholar
- Inesa, Toska. 2019. Teaching Legal English in multilevel classes. The European Journal of Education and Applied Psychology. 15--22. https://doi.org/10.29013/EJEAP-19-3-15-22Google Scholar
- Long, Michael. 1991. Focus on form: a design feature in language teaching methodology. In de Bot, K., Ginsberg, R. B., & Kramsch, C. (Eds.), Foreign language research in cross-cultural perspectives, 39--52. Amsterdam, the Netherlands: Benjamins. http://dx.doi.org/10.1075/sibil.2.07lonGoogle Scholar
- Medrea, Nicoleta. 2012. Cross-cultural communication: a challenge to English for legal purposes. Procedia - Social and Behavioral Sciences, 46, 5475 - 5479. https://10.1016/j.sbspro.2012.06.460Google ScholarCross Ref
- Mason, Catherine, Atkins, Rosemary. 2007. The lawyer's English language coursebook. Stamford: Global Legal English.Google Scholar
- Nino, Ana. 2009. Machine Translation in Foreign Language Learning: Language Learners' and Tutors' Perceptions of Its Advantages and Disadvantages. ReCALL: the Journal of EUROCALL 21.2 (May 2009) 241--258. Google ScholarDigital Library
- Northcott, Jill. 2013. Legal English. In B. Paltridge, & S. Starfield (Eds.), The Handbook of English for specific purposes (pp. 213--226). Wiley-Blackwell. https://doi.org/10.1002/9781118339855.ch11Google Scholar
- Richards, Keith. 2009. Trends in qualitative research in language teaching since 2000. Language Teaching, 42(2), 147-180. https://doi.org/10.1017/S0261444808005612Google ScholarCross Ref
- Rubtsova, Anna. 2019. Socio-linguistic innovations in education: productive implementation of intercultural communication. IOP Conference Series: Materials Science and Engineering, 497. https://doi.org/10.1088/1757-899X/497/1/012059Google ScholarCross Ref
- Sãrcěvić, Susan. 1997. New approach to legal translation. Alphen aan den Rijn: Kluwer Law International, 308 p.Google Scholar
- Sãrcěvić Susan. 1989. Conceptual Dictionaries for Translation in the Field of Law. International Journal of Lexicography. № 2:4, 277--293.Google Scholar
- Schane, Sanford. 2006. Language and the law. London: Continuum. https://doi.org/10.1017/S004740450808069XGoogle Scholar
- Sierocka, Halina. 2016. Action research in designing and implementing courses of English for legal purposes. Studies in logic, grammar and rhetoric, 45(58), 225--251. https://doi.org/10.1515/slgr-2016-0026Google Scholar
- Sharma, Pete, Barrett, Barney. 2007. Blended Learning: Using Technology in and Beyond the Language Classroom. Macmillan books for teachers: Macmillan.Google Scholar
- Venuti, Lawrence. 1995. The translator's invisibility. Routledge. https://doi.org/10.5195/jffp.1997.390Google Scholar
- Widdowson, Henry. 1990. Aspects of language teaching. Oxford University Press. https://doi.org/10.18172/cif.2434Google Scholar
- Williams, Christopher. 2005. Tradition and change in legal English: verbal constructions in prescriptive texts. Peter Lang. https://doi.org/10.1016/j.pragma.2006.09.009Google Scholar
- Yang, W. 2010. Brief Study on Domestication and Foreignization in Translation. Journal of Language Teaching and Research. Vol. 1, No., 77--80.Google ScholarCross Ref
Index Terms
- Machine translation and legal tech in legal translation training
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