Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles
<p dir="ltr">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...
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513528322326528 |
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| author | Hussein Abu-Rayyash (17900036) |
| author2 | Ahmad S. Haider (17900039) Amer Al-Adwan (17316982) |
| author2_role | author author |
| author_facet | Hussein Abu-Rayyash (17900036) Ahmad S. Haider (17900039) Amer Al-Adwan (17316982) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hussein Abu-Rayyash (17900036) Ahmad S. Haider (17900039) Amer Al-Adwan (17316982) |
| dc.date.none.fl_str_mv | 2023-01-30T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1057/s41599-023-01506-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Strategies_of_translating_swear_words_into_Arabic_a_case_study_of_a_parallel_corpus_of_Netflix_English-Arabic_movie_subtitles/25139708 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Language, communication and culture Language studies Linguistics Audiovisual translation (AVT) Multimedia translation Transfer of verbal and non-verbal aspects Multi-semiotic translation Subtitling Dubbing Intralingual subtitles Interlingual subtitles Bilingual subtitles Corpus linguistics Corpus-assisted translation studies Translation strategies Swear words Taboo language Modern Standard Arabic (MSA) Arabic vernaculars Politeness in language Denotative meaning Connotative meaning Language and culture |
| dc.title.none.fl_str_mv | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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, namelyfuck, shit, damn, ass, bitch, bastard, asshole, dick, cunt, andpussy. 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Humanities and Social Sciences Communications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1057/s41599-023-01506-3" target="_blank">https://dx.doi.org/10.1057/s41599-023-01506-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c05d4e1cc499214738888ec8297ae92a |
| identifier_str_mv | 10.1057/s41599-023-01506-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25139708 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitlesHussein Abu-Rayyash (17900036)Ahmad S. Haider (17900039)Amer Al-Adwan (17316982)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationLanguage, communication and cultureLanguage studiesLinguisticsAudiovisual translation (AVT)Multimedia translationTransfer of verbal and non-verbal aspectsMulti-semiotic translationSubtitlingDubbingIntralingual subtitlesInterlingual subtitlesBilingual subtitlesCorpus linguisticsCorpus-assisted translation studiesTranslation strategiesSwear wordsTaboo languageModern Standard Arabic (MSA)Arabic vernacularsPoliteness in languageDenotative meaningConnotative meaningLanguage and culture<p dir="ltr">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, namelyfuck, shit, damn, ass, bitch, bastard, asshole, dick, cunt, andpussy. 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Humanities and Social Sciences Communications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1057/s41599-023-01506-3" target="_blank">https://dx.doi.org/10.1057/s41599-023-01506-3</a></p>2023-01-30T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1057/s41599-023-01506-3https://figshare.com/articles/journal_contribution/Strategies_of_translating_swear_words_into_Arabic_a_case_study_of_a_parallel_corpus_of_Netflix_English-Arabic_movie_subtitles/25139708CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/251397082023-01-30T03:00:00Z |
| spellingShingle | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles Hussein Abu-Rayyash (17900036) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Language, communication and culture Language studies Linguistics Audiovisual translation (AVT) Multimedia translation Transfer of verbal and non-verbal aspects Multi-semiotic translation Subtitling Dubbing Intralingual subtitles Interlingual subtitles Bilingual subtitles Corpus linguistics Corpus-assisted translation studies Translation strategies Swear words Taboo language Modern Standard Arabic (MSA) Arabic vernaculars Politeness in language Denotative meaning Connotative meaning Language and culture |
| status_str | publishedVersion |
| title | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| title_full | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| title_fullStr | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| title_full_unstemmed | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| title_short | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| title_sort | Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Language, communication and culture Language studies Linguistics Audiovisual translation (AVT) Multimedia translation Transfer of verbal and non-verbal aspects Multi-semiotic translation Subtitling Dubbing Intralingual subtitles Interlingual subtitles Bilingual subtitles Corpus linguistics Corpus-assisted translation studies Translation strategies Swear words Taboo language Modern Standard Arabic (MSA) Arabic vernaculars Politeness in language Denotative meaning Connotative meaning Language and culture |