This study addresses a binary classification task to determine whether a text sequence, either a sentence or paragraph, is subjective or objective. The task spans five languages: Arabic, Bulgarian, English, German, and Italian, along with a multilingual category. Our approach involved several key techniques. Initially, we preprocessed the data through parts of speech (POS) tagging, identification of question marks, and application of attention masks. We fine-tuned the sentiment-based Transformer model 'MarieAngeA13/Sentiment-Analysis-BERT' on our dataset. Given the imbalance with more objective data, we implemented a custom classifier that assigned greater weight to objective data. Additionally, we translated non-English data into English to maintain consistency across the dataset. Our model achieved notable results, scoring top marks for the multilingual dataset (Macro F1=0.7121) and German (Macro F1=0.7908). It ranked second for Arabic (Macro F1=0.4908) and Bulgarian (Macro F1=0.7169), third for Italian (Macro F1=0.7430), and ninth for English (Macro F1=0.6893).

本研究对二分类任务进行了探讨，以确定一个文本序列（句子或段落）是否主观或客观。研究涵盖了五种语言：阿拉伯语、保加利亚语、英语、德语和意大利语，以及跨语言类别。我们的方法包括了几个关键技术：通过词性标注对数据进行预处理、识别问号和应用注意力掩码。我们在我们的数据集上对情感基础的Transformer模型'MarieAngeA13/Sentiment-Analysis-BERT'进行了微调。鉴于客观数据较多的不平衡情况，我们实现了一种自定义分类器，给予客观数据更大的权重。此外，我们将非英语数据翻译成英语，以保持数据集的一致性。我们的模型取得了显著的结果，在跨语言数据集（Macro F1=0.7121）和德语（Macro F1=0.7908）方面获得了最高分。在阿拉伯语（Macro F1=0.4908）和保加利亚语（Macro F1=0.7169）方面排名第二，意大利语（Macro F1=0.7430）排名第三，在英语（Macro F1=0.6893）方面排名第九。

CheckThat! 2024中的Nullpointer：从多语种文本序列中识别主观性