检测文本正式度：文本分类方法研究
Detecting Text Formality: A Study of Text Classification Approaches
AI总结：
本研究首次系统探讨了基于统计、神经网络和Transformer的文本正式度检测方法，通过单语、多语和跨语种实验发现Char BiLSTM在单语和多语任务上更优，而Transformer在跨语种迁移上更稳定。
摘要：
正式度是文本文档的重要特征之一。自动检测文本的正式度水平对各种自然语言处理任务具有潜在益处。此前，已有两个包含多语言正式度标注的大规模数据集被引入——GYAFC和X-FORMAL。然而，它们主要用于训练风格迁移模型。同时，文本正式度检测本身也可能是一项有用的应用。据我们所知，本研究首次系统地探讨了基于统计、基于神经网络的以及基于Transformer的机器学习方法的正式度检测方法，并提供了性能最佳的模型供公众使用。我们进行了三种类型的实验——单语、多语和跨语种。研究表明，在单语和多语正式度分类任务中，Char BiLSTM模型优于基于Transformer的模型，而基于Transformer的分类器在跨语种知识迁移方面更为稳定。
英文摘要：
Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation -- GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments -- monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
