Unsupervised Domain Adaptation (UDA) is a popular technique that aims to
reduce the domain shift between two data distributions. It was successfully
applied in computer vision and natural language processing. In the current
work, we explore the effects of various unsupervised domain adaptation
techniques between two text classification tasks: fake and hyperpartisan news
detection. We investigate the knowledge transfer from fake to hyperpartisan
news detection without involving target labels during training. Thus, we
evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive
learning. Extensive experiments show that these techniques improve performance,
while including data augmentation further enhances the results. In addition, we
combine clustering and topic modeling algorithms with UDA, resulting in
improved performances compared to the initial UDA setup.

比较无监督领域自适应技术在两个文本分类任务（假新闻检测和偏激新闻检测）中的效果，并探讨了数据增强、聚类和主题建模与无监督领域自适应技术相结合对性能的提升。

从虚假新闻到极端党派新闻的检测：使用领域自适应技术

From Fake to Hyperpartisan News Detection Using Domain Adaptation

In this paper, we describe our submission to SemEval-2019 Task 4 on
Hyperpartisan News Detection. Our system relies on a variety of engineered
features originally used to detect propaganda. This is based on the assumption
that biased messages are propagandistic in the sense that they promote a
particular political cause or viewpoint. We trained a logistic regression model
with features ranging from simple bag-of-words to vocabulary richness and text
readability features. Our system achieved 72.9% accuracy on the test data that
is annotated manually and 60.8% on the test data that is annotated with distant
supervision. Additional experiments showed that significant performance
improvements can be achieved with better feature pre-processing.

本文报道了我们在 SemEval-2019 任务 4 中提出的超级党派新闻检测系统，该系统主要使用一些原本用于检测宣传的工程特征。我们训练了一个基于逻辑回归模型的分类器，并使用从简单单词袋到词汇丰富度和文本可读性等特征来训练它。我们的系统在手动注释的测试数据上实现了 72.9% 的准确率，而在带有远程监督标注的测试数据上则实现了 60.8% 的准确率。另外的实验表明，使用更好的特征预处理可以大幅提高性能。