用AI自动翻译字幕，让多语言影视更生动
Hermes the Polyglot: A Unified Framework to Enhance Expressiveness for Multimodal Interlingual Subtitling
- 分角色识别+术语提取+表达力增强三模块协同
- 字幕翻译既准确又自然，上下文连贯性提升
- 适合影视本地化、跨语言内容创作人群
跨语言字幕翻译对影视娱乐本地化至关重要，但尚未在机器翻译领域得到充分研究。尽管大语言模型（LLMs）显著提升了机器翻译的通用能力，但字幕文本特有的语义连贯性、代词与术语翻译、表达力等问题仍难解决。为此，我们提出Hermes，一个基于LLM的自动化字幕翻译框架。该框架集成三个模块：说话人区分、术语识别和表达力增强，有效应对上述挑战。实验表明，Hermes在说话人区分任务上达到当前最优性能，并生成表达丰富、上下文一致的翻译结果，推动了跨语言字幕翻译的研究进展。
原文摘要 · Abstract (English)
Interlingual subtitling, which translates subtitles of visual media into a target language, is essential for entertainment localization but has not yet been explored in machine translation. Although Large Language Models (LLMs) have significantly advanced the general capabilities of machine translation, the distinctive characteristics of subtitle texts pose persistent challenges in interlingual subtitling, particularly regarding semantic coherence, pronoun and terminology translation, and translation expressiveness. To address these issues, we present Hermes, an LLM-based automated subtitling framework. Hermes integrates three modules: Speaker Diarization, Terminology Identification, and Expressiveness Enhancement, which effectively tackle the above challenges. Experiments demonstrate that Hermes achieves state-of-the-art diarization performance and generates expressive, contextually coherent translations, thereby advancing research in interlingual subtitling.
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