International Journal of Philology

The use of computer tools in the modern translation of agricultural texts

Anna Monashnenko, Gelena Lugova
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Abstract

Due to the growing role of the Internet in the international exchange of information, the demand for rapid translation as a means of communication is growing rapidly. In order to meet the needs of document management, scientific and technical documentation, and the exchange of experience, modern professional translators are turning to software developments to optimise their work. The purpose of this study was to analyse the advantages and disadvantages of using computer tools for translating agricultural texts from German to Ukrainian, with a particular focus on the application of machine translation systems. This paper provided an overview of modern computer tools and a comparative analysis of problematic aspects of translating agricultural texts from German to Ukrainian. For the study, the author used Google and DeepL machine translation systems and the method of manual assessment of the quality of the resulting text. Given that the use of computer tools is perceived as a manifestation of technological progress and can significantly increase the productivity of a professional translator, this study focused on the analysis of the use of computer tools in the translation of agricultural texts from German into Ukrainian. In addition, the paper presented a comparative analysis of two machine translation systems and the specific features of the post-editing process. In the current environment, when speed is becoming increasingly important and a prerequisite for competitiveness, machine translation is becoming a service offered by translation companies and professional translators via the Internet. Online services provide many users with the opportunity to receive instant translations when the quality of the translation is not of fundamental importance. Professional automated translation tools. Considering that the spread of MT systems and other software products in translation activity has significantly altered the conventional approach to working with text, this study aimed to identify the challenges associated with using computer tools in the translation of agricultural texts and ways to address them. Under these conditions, the need to acquire skills in working with MT systems is becoming increasingly urgent

Keywords

post-editing; translation automation; machine translation; computer translation; translation memory; source language; target language

Suggested citation
Monashnenko, A., & Lugova, G. (2024). The use of computer tools in the modern translation of agricultural texts. International Journal of Philology, 15(3), 57-68.
References

[1] Ahrenberg, L. (2017). Comparing machine translation and human translation: A case study. In I. Temnikova, C. Orasan, G. Corpas & S. Vogel (Eds). RANLP 2017 the first workshop on human-informed translation and interpreting technology (HiT-IT) proceedings of the workshop (pp. 21-28). Varna: Association for Computational Linguistics, Shoumen.

[2] Alcina, A. (2008). Translation technologies: Scope, tools and resources. Target: International Journal of Translation Studies, 20(1), 79-102. doi: 10.1075/target.20.1.05alc.

[3] Banitz, B. (2021). Machine translation: A critical look at the performance of rule-based and statistical machine translation. Retrieved from https://www.academia.edu/71258139/Machine_Translation_A_Critical_Look_at_the_Performance_of_Rule_Based_and_Statistical_Mac.

[4] Babych, B., (2013). Comparative evaluation of two machine translation systems. Woodhouse: University of Leeds.

[5] DeepL Translate. (n.d.). Retrieved from https://www.deepl.com/uk/translator.

[6] Diehl, D. (2024). How fair are Fairtrade products?. Retrieved from https://www.tagesschau.de/wirtschaft/verbraucher/fairtrade-siegel-einzelhandel-100.html.

[7] European Commission. (n.d.). iTranslate4: Internet translators for all European languages (iTranslate4). Retrieved from https://surl.li/tvecei.

[8] European policy with prospects for agriculture, forestry and rural areas. (n.d.). Retrieved from https://agriculture.ec.europa.eu/common-agricultural-policy/rural-development_de.

[9] European policy with prospects for agriculture, forestry and rural areas. (2024). Retrieved from https://surl.li/jhyyfg.

[10] Göggerle, T. (2023). Optimised combine harvesting. Harvest faster: Farmer has an ingenious invention for combine harvesters. Retrieved from https://www.agrarheute.com/technik/ackerbautechnik/schneller-ernten-landwirt-hat-geniale-erfindung-fuer-maehdrescher-608470.

[11] Kenny, D., & Quah, C.K. (2006). Translation and technology. Machine Translation, 20(5), 23.

[12] Krüger, R. (2016). Contextualising computer-assisted translation tools and modelling their usability. Trans-com, 9(1), 114-148.

[13] META.ua. (n.d.). Retrieved from https://translate.meta.ua/ua/.

[14] Moran Vallejo, A. (2019). The translation of Spanish agri-food texts into English and Italian using machine translation engines: A contrastive study. Valladolid: University of Valladolid.

[15] Nuscheler, C. (2024). Catch crops in flight: 15 minutes per hectare: These farmers swear by drone sowing. Retrieved from https://www.agrarheute.com/pflanze/zwischenfruechte/15-minuten-pro-hektar-diese-landwirte-schwoeren-aussaat-drohne-623839.

[16] Pettersson, E. (2004). The machine translation system MATS: Past, present and future. Retrieved from https://www.academia.edu/117385010/The_machine_translation_system_MATS_past_present_a_future.

[17] Pushuk, N. (2021). Machine translation and its principles of classification. In Trends in development of innovative scientific research in the context of global changes (pp. 68-71). Riga. doi: 10.30525/978-9934-26-076-6-21.

[18] Rawe, Y. (2024). Free of ragwort in 4 years: How a farmer treats his grassland. Retrieved from https://www.agrarheute.com/tier/rind/4-jahren-frei-jakobskreuzkraut-so-therapiert-landwirt-gruenland-620959.

[19] Schürer, J. (2023). Why farmer Bützler is now investing in a biogas plant. Retrieved from https://www.agrarheute.com/energie/gas/landwirt-buetzler-noch-biogasanlage-investiert-623922.

[20] Stevanović, I., & Radičević, L. (2020). Comparative analysis of machine translation systems. Retrieved from https://www.researchgate.net/publication/348402527_Comparative_Analysis_of_Machine_Translation_Systems.

[21] Translate.eu. (n.d.). Retrieved from https://www.translate.eu/.

[22] TUT.ua. (n.d.). Retrieved from https://tut.ua/.

[23] Veselovska, H., & Radetska, S. (2021). Machine translation: Its typology, advantages and disadvantages. Current Issues of Humanitarian Sciences, 7(35), 1-5. doi: 10.24919/2308-4863/35-7-4

[24] Voiteko, K. (2010). Features of translating agricultural literature and general principles for translating terminological borrowings in the field of crop production. Retrieved from https://er.nau.edu.ua/handle/NAU/23011.

[25] Weijnitz, P., Forsbom, E., Gustavii, E., Pettersson, E., & Tiedemann, J. (2015). MT goes farming: Comparing two machine translation approaches on a new domain. Retrieved from https://www.academia.edu/21905912/MT_goes_farming_Comparing_two_machine_translation_approaches_on_a_new_domain.

[26] Yamada, M. (2019). The impact of Google machine translation on post-editing by student translators. The Journal of Specialised Translation, 31, 87-106.