仅用100行文本就能捕捉个人写作风格并跨语言迁移。
StAyaL | Multilingual Style Transfer
- 用少量文本生成高维风格嵌入，分离内容与风格
- 跨语言风格迁移准确率达74.9%，F1为0.75
- 适合个性化内容生成与多语言风格转换场景
风格化文本生成在提升沟通表现力方面至关重要，能体现个体表达的细微差异。本文提出一种新方法，可在不同语言间生成特定说话人的风格文本。仅需100行文本，即可将个体独特风格建模为高维嵌入，用于文本生成和风格翻译。该方法通过三阶段实现：利用外部语料增强说话人数据、采用机器学习与深度学习技术分离风格与内容、通过对学习到的嵌入进行均值池化生成抽象风格表征。实验表明该方法具备主题无关性，测试准确率为74.9%，F1得分为0.75。结果验证了风格表征在多语言交流中的潜力，为个性化内容生成与跨语言风格迁移开辟新路径。
原文摘要 · Abstract (English)
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.
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