Neural Machine Translation Strategies for Rendering Persian Slangs in Audiovisual Materials: Iranian EFL Teachers and Students in Focus

Subject Areas : All areas of language and translation

Mohammad Amin Mozaheb ^{1 *} , Ali Salami ² , Amir Ghajarieh ³ , Sara Jafari ⁴

1 - Department of Foreign Languages, Imam Sadiq University, Tehran, Iran
2 - English Department, Faculty of Foreign Languages, University of Tehran, Tehran, Iran
3 - Department of TEFL, Ershad Damavand University, Tehran, Iran
4 - Department of TEFL, Ershad Damavand University, Tehran, Iran

Received: 2023-04-04 Accepted : 2023-06-12 Published : 2024-04-25

Keywords: Neural Machine Translation (NMT), Slang, Audiovisual Translation, Translation Strategies,

Abstract :

This study aims to examine how neural machine translation systems render slang terms in audiovisual materials. To this end, 100 dialogues from the Iranian award-winning film 'Sheeple' (2018) by Houman Seyyedi were randomly selected and analyzed in order to identify Persian slang terms. Three neural translation machines were employed to translate these terms. Baker's (1992, 2011, 2018) strategies were applied to each neural translation output. After analyzing the results, it was determined that Google Translate had the highest frequency of "Translation by Paraphrase Using an Unrelated Word" while Targoman and Farazin mostly used "Translation by Paraphrase Using a Related Word". This study also examined the naturalness of translation based on Nida and Taber(2003) by asking the opinions of five experts regarding the equivalents proposed by neural machine translation. Although Targoman’s performance is far from perfect in comparison to human translation, the data analysis indicates that Targoman is the most natural of the three translation machines. Ultimately, the results were shared by TEFL students in an Iranian university. A large number of these slang terms were unfamiliar to the students in their second language. Besides implications for teaching English translation and the English language, this study is beneficial for developers of neural machine translation systems who strive to improve the quality of machine translation.

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