The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task~2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered dataset, using data from multiple languages. Finally, the three approaches are combined in a simple majority voting ensemble, resulting in 0.77 macro F1 on the test set and achieving 2nd place on the English subtask.

社交网络的广泛使用导致互联网上存在主观、误导甚至虚假信息。因此，主观性检测可以在确保信息的客观性和质量方面发挥重要作用。本文介绍了Gpachov团队在CLEF-2023 CheckThat！实验室任务2中构建的解决方案，该解决方案涉及三个不同的研究方向，采用了微调句子嵌入编码器模型和降维、样本高效少样本学习模型以及多语言数据集上微调多语言转换器等方法，并通过简单多数投票集成的方式融合三种方法，在测试集上达到0.77的宏F1，并在英语子任务上获得第二名。

Gpachov在CheckThat！2023中的新闻文章主观性检测的多元多途径集成