Faculty of languages, October University for Modern Sciences and Arts, Cairo, Egypt.
PLoS One. 2024 Oct 21;19(10):e0311020. doi: 10.1371/journal.pone.0311020. eCollection 2024.
Movies often use allusions to add depth, create connections, and enrich the storytelling. However, translators may face challenges when subtitling movie allusions, as they must render both meaning and culture accurately despite existing language and cultural barriers. These challenges could be further complicated by the use of available AI tools attempting to subtitle movie allusions, while probably unaware of existing cultural complexities. This research investigates these challenges using qualitative and descriptive quantitative approaches by analyzing the movie Birdman or (The Unexpected Virtue of Ignorance), comprising13.014 words, to identify the types of allusions used and compare the human- vs. AI (ChatGPT)-generated Arabic subtitles in terms of the subtitling strategies, their frequency, and quality. The results revealed that the movie used 52 Noun Phrase (NP) allusions, where the writer intertextually employed a proper name to convey meaning, and 8 Key-Phrase (KP) allusions, where the writer used phrases that convey implicit meaning easily perceived by members of the source culture (by referring to religious, literary, or entertainment texts). For NP allusions, both the human translator and AI opted for retentive strategies; however, the human translator's preference to add guidance/parentheses to mark NP allusions was distinct. Additionally, it was observed that AI used neologism to render technology-related allusions, which could be a suggested strategy for NP subtitling into Arabic. For KP allusions, while the human translator seemed to be cognizant of the idea that KP allusions typically require a change in wording, AI fell short. Specifically, the human translator employed reduction in 5 out of 8 KPs, opting for minimum change/literal translation only three times. Conversely, AI utilized literal translation in all 8 examples, despite its awareness of the allusion and its intricate meaning/reference. As for the FAR assessment, for NP allusions, it revealed minor semantic errors in AI's subtitles that did not affect the plot. Regarding KP allusions, AI's subtitles were penalized in 5 out of its 8 Arabic renditions, in contrast to the human translator. Most of the errors were serious semantic errors that likely disrupted the flow of reading the subtitles due to conveying irrelevant meanings in the movie's/scene's context. Despite its functionality, this study suggests adding an extra parameter to the FAR model: consistency, as it plays a role in enhancing audience involvement and understanding. Its absence, as observed in some AI instances, can be misleading.
电影中常使用典故来增加深度、建立联系和丰富故事叙述。然而，翻译电影典故时，翻译人员可能会面临挑战，因为他们必须在存在语言和文化障碍的情况下准确传达意义和文化。由于可用的人工智能工具在尝试翻译电影典故时可能没有意识到现有的文化复杂性，这些挑战可能会更加复杂。本研究通过使用定性和描述性定量方法分析电影《鸟人》（或《无知的意外之美》）（共 13014 个单词）来研究这些挑战，以确定使用的典故类型，并比较人工翻译与人工智能（ChatGPT）生成的阿拉伯语字幕在字幕策略、频率和质量方面的差异。研究结果表明，电影使用了 52 个名词短语（NP）典故，其中作者通过互文性使用专有名词来传达意义，以及 8 个关键词短语（KP）典故，其中作者使用了那些能够传达源文化中容易被成员理解的隐含意义的短语（通过参考宗教、文学或娱乐文本）。对于 NP 典故，人工翻译和人工智能都选择了保留策略；然而，人工翻译偏好添加指导/括号来标记 NP 典故的做法是不同的。此外，还观察到人工智能使用新词来翻译与技术相关的典故，这可能是将 NP 字幕翻译成阿拉伯语的一种建议策略。对于 KP 典故，虽然人工翻译似乎意识到 KP 典故通常需要改变措辞，但人工智能却没有做到这一点。具体来说，人工翻译在 8 个 KP 中有 5 个选择了减少，只选择了最小的变化/直译 3 次。相反，人工智能在 8 个例子中都使用了直译，尽管它意识到了典故的存在及其复杂的意义/参考。就 FAR 评估而言，对于 NP 典故，它揭示了人工智能字幕中的一些轻微语义错误，但这些错误并没有影响情节。至于 KP 典故，人工智能的 8 个阿拉伯语翻译中有 5 个被扣分，而人工翻译则没有。大多数错误都是严重的语义错误，由于在电影/场景的上下文中传达了不相关的含义，可能会破坏字幕阅读的流畅性。尽管人工智能具有功能，但本研究建议在 FAR 模型中添加一个额外的参数：一致性，因为它在增强观众参与度和理解方面发挥着作用。在某些人工智能实例中观察到的这种缺失可能会产生误导。
