This study adopts a corpus-assisted approach to explore the translation strategies that Netflix subtitlers opted for in rendering 1564 English swear words into Arabic. It uses a 699,229-word English-Arabic parallel corpus consisting of the English transcriptions of forty English movies, drama, action, science fiction (sci-fi), and biography and their Arabic subtitles. Using the wordlist tool in SketchEngine, the researchers identified some frequent swear words, namely fuck, shit, damn, ass, bitch, bastard, asshole, dick, cunt, and pussy. Moreover, using the parallel concordance tool in SketchEngine revealed that three translation strategies were observed in the corpus, namely, omission, softening, and swear-to-non-swear. The omission strategy accounted for the lion’s share in the investigated data, with 66% for drama, 61% for action, 52% for biography, and 40% for sci-fi. On the other hand, the swear-to-non-swear strategy was the least adopted one, accounting for 21% in sci-fi, 16% in biography, 14% in drama, and 11% in action. In addition, the softening strategy got the second-highest frequency across the different movie genres, with 39% for sci-fi, 32% for biography, 28% for action, and 20% for drama. Since swear words have connotative functions, omitting or euphemizing them could cause a slight change in the representation of meaning and characters. The study recommends more corpus-assisted studies on different AVT modes, including dubbing, voiceover, and free commentaries.

This study investigates Netflix translation of English movie lyrics into Arabic. It examines and categorizes the subtitles of the movie lyrics based on the translation options proposed by Franzon. Translation options refer to the different methods or approaches a translator can use when translating a song. A parallel corpus of 60 lyrics extracted from 10 musical movies was compiled by aligning the English script and Arabic subtitles. The researchers found that Netflix’s subtitlers employed four options for rendering English lyrics into Arabic. These are neglecting the music in translating the lyric (literal subtitling), which was used in rendering 60% of the investigated lyrics, not translating the lyrics (deletion), which was observed in 17% of the cases, and adapting the music to the translation (esthetic subtitling) was followed in rendering only five lyrics (8%). Finally, incorporating the three previous options (blended subtitling), which was adopted in subtitling 15% of the investigated data. This study recommends further research on the audience reception of the different subtitling options of lyrics. The findings of the current study can be useful for subtitlers and translation students, especially those interested in literary translation and musical movie translation.