Abstract
Stylistic text generation plays a vital role in enhancing communication by reflecting the nuances of individual expression. This paper presents a novel approach for generating text in a specific speaker's style across different languages. We show that by leveraging only 100 lines of text, an individuals unique style can be captured as a high-dimensional embedding, which can be used for both text generation and stylistic translation. This methodology breaks down the language barrier by transferring the style of a speaker between languages. The paper is structured into three main phases: augmenting the speaker's data with stylistically consistent external sources, separating style from content using machine learning and deep learning techniques, and generating an abstract style profile by mean pooling the learned embeddings. The proposed approach is shown to be topic-agnostic, with test accuracy and F1 scores of 74.9\% and 0.75, respectively. The results demonstrate the potential of the style profile for multilingual communication, paving the way for further applications in personalized content generation and cross-linguistic stylistic transfer.
Abstract (translated)
风格化的文本生成在通过反映个人表达的细微差别来增强沟通方面扮演着至关重要的角色。本文提出了一种新颖的方法，用于跨不同语言以特定说话者的风格生成文本。我们展示了仅利用100行文本即可捕捉到个体独特风格的高维嵌入，并且这种嵌入可用于文本生成和风格化翻译。该方法通过在语言间转移发言人的风格来打破语言障碍。 本文按照三个主要阶段展开：使用与说话者数据风格一致的外部来源增强其数据，利用机器学习和深度学习技术将风格从内容中分离出来，以及通过对所学嵌入进行均值池化生成抽象的风格配置文件。所提出的这种方法被证明是主题无关的，在测试准确性和F1分数方面分别达到了74.9%和0.75。 实验结果展示了风格配置文件在多语言沟通中的潜力，并为个性化内容生成和个人化的跨语种风格转换应用开辟了道路。
URL
https://arxiv.org/abs/2501.11639
