[Burchardt, Aljoscha, et al. 2014. Multidimensional Quality Metrics (MQM) Definition. http://www.qt21.eu/mqm-definition/definition-2014-08-14.html (accessed May 7, 2019).]Search in Google Scholar
[Burchardt, Aljoscha, and Jörg Porsiel. 2017. Vorwort: Was kann die maschinelle Übersetzung und was nicht? In Maschinelle Übersetzung. Grundlagen für den professionellen Einsatz, ed. Jörg Porsiel, 11–8. Berlin: BDÜ-Fachverlag.]Search in Google Scholar
[Castilho, Sheila, et al. 2017. Is Neural Machine Translation the New State of the Art? The Prague Bulletin of Mathematical Linguistics 108: 109–20.10.1515/pralin-2017-0013]Search in Google Scholar
[Forcada, Mikel L. 2017. Making sense of neural machine translation. Translation Spaces 6/2: 291–309.10.1075/ts.6.2.06for]Search in Google Scholar
[Hansen-Schirra, Silvia, et al. 2017. Post-Editing: Strategien, Qualität, Effizienz. In Maschinelle Übersetzung. Grundlagen für den professionellen Einsatz, ed. Jörg Porsiel, 176–91. Berlin: BDÜ-Fachverlag.]Search in Google Scholar
[Heiss, Christine, and Marcello Soffritti. 2018. DeepL Traduttore e didattica della traduzione dall’italiano in tedesco. Alcune valutazioni preliminari. InTRAlinea. Special Issue: Translation and Interpreting for Language Learners (TAIL).http://www.intralinea.org/specials/article/2294 (accessed September 13, 2019).]Search in Google Scholar
[Hutchins, W. John. 1995. Machine Translation: A Brief History In Concise History of the Language Sciences: from the Sumerians to the Cognitivists, eds. Ernst F. K. Koerner and R.E. Asher, 431–45. Oxford: Pergamon Press.10.1016/B978-0-08-042580-1.50066-0]Search in Google Scholar
[Killman, Jeffrey. 2014. Vocabulary Accuracy of Statistical Machine Translation in the Legal Context. In Third Workshop on Post-Editing Technology and Practice, eds. Sharon O’Brian, Michel Simard and Lucia Specia, 85–98. www.amtaweb.org/AMTA2014Proceedings/AMTA2014Proceedings_PEWorkshop_final.pdf (accessed May 7, 2019).]Search in Google Scholar
[Koehn, Philipp. 2010. Statistical Machine Translation. Cambridge: Cambridge University Press.10.1017/CBO9780511815829]Search in Google Scholar
[Kyburz, Kevin. 2018. Schlägt sogar Google. Die Übersetzungsmaschine DeepL. https://techgarage.blog/schlaegt-sogar-google-dieuebersetzungsmaschine-deepl/ (accessed February 28, 2019).]Search in Google Scholar
[Matthiesen, Aaron J. 2017. Maschinelle Übersetzung im Wandel. Die Auswirkungen von künstlicher Intelligenz auf maschinelle Übersetzungssysteme. Mit einer vergleichenden Untersuchung von Google Translate und Microsoft Translator. Berlin: epubli.]Search in Google Scholar
[Prieto Ramos, Fernando. 2015. Quality Assurance in Legal Translation: Evaluating Process, Competence and Product in the Pursuit of Adequacy. International Journal for the Semiotics of Law – Revue internationale de Sémiotique juridique 28/1: 11–30.]Search in Google Scholar
[Şahin, Mehmet, and Nilgün Dungan. 2014. Translation testing and evaluation: A study on methods and needs. Translation & Interpreting 6/2: 67–90.]Search in Google Scholar
[Van Brussel, Laura, et al. 2018. A Fine-grained Error Analysis of NMT, PBMT and RBMT Output for English-to-Dutch. In Eleventh International Conference on Language Resources and Evaluation, 3799–804. https://biblio.ugent.be/publication/8561558 (accessed May 8, 2019)]Search in Google Scholar
[Wallberg, Ilona. 2017. DIN EN ISO 18587 – eine Norm über den Prozess des Posteditierens. In Maschinelle Übersetzung. Grundlagen für den professionellen Einsatz, ed. Jörg Porsiel, 160–7. Berlin: BDÜ-Fachverlag.]Search in Google Scholar
[Werthmann, Antonia, and Andrea Witt. 2014. Maschinelle Übersetung – Gegenwart und Perspektiven. In Translation and Interpretation in Europe. Contributions to the Annual Conference 2013 of EFNIL in Vilnius, ed. Gerhard Stickel, 79–103. Frankfurt et al.: Lang.]Search in Google Scholar
[Yates, Sarah. 2006. Scaling the Tower of Babel Fish: An Analysis of the Machine Translation of Legal Information. Law Library Journal 98/3: 481–500.]Search in Google Scholar
