Abstract
We address the challenging task of neural machine translation (NMT) in the entertainment domain, where the objective is to automatically translate a given dialogue from a source language content to a target language. This task has various applications, particularly in automatic dubbing, subtitling, and other content localization tasks, enabling source content to reach a wider audience. Traditional NMT systems typically translate individual sentences in isolation, without facilitating knowledge transfer of crucial elements such as the context and style from previously encountered sentences. In this work, we emphasize the significance of these fundamental aspects in producing pertinent and captivating translations. We demonstrate their significance through several examples and propose a novel framework for entertainment translation, which, to our knowledge, is the first of its kind. Furthermore, we introduce an algorithm to estimate the context and style of the current session and use these estimations to generate a prompt that guides a Large Language Model (LLM) to generate high-quality translations. Our method is both language and LLM-agnostic, making it a general-purpose tool. We demonstrate the effectiveness of our algorithm through various numerical studies and observe significant improvement in the COMET scores over various state-of-the-art LLMs. Moreover, our proposed method consistently outperforms baseline LLMs in terms of win-ratio.
Abstract (translated)
我们解决了娱乐领域中的神经机器翻译（NMT）难题，目标是将源语言的内容对话自动翻译成目标语言。这项任务在自动配音、字幕生成和其他内容本地化方面有着广泛的应用，有助于让更多观众接触到原版内容。传统的NMT系统通常孤立地翻译单个句子，并且无法有效传递之前句子中的关键元素（如上下文和风格）的知识。在这项工作中，我们强调了这些基本因素在产生相关性和吸引力的翻译中所起的关键作用。通过几个示例证明其重要性，我们提出了一种新型框架用于娱乐领域内的翻译工作，据我们所知这是首次尝试。此外，我们还引入了一种算法来估计当前会话中的上下文和风格，并利用这些估算值生成一个提示，指导大型语言模型（LLM）生成高质量的翻译文本。我们的方法对于不同语言和LLM均适用，因此具有通用性。通过多种数值研究证明了该算法的有效性，并且在COMET评分上相对于各种最先进的LLM显著提升。此外，在胜率方面，我们提出的方法始终优于基线LLMs。
URL
https://arxiv.org/abs/2412.20440
