Nullpointer at CheckThat! 2024: 从多语言文本序列中识别主观性
Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text Sequence
AI总结：
本研究针对多语言文本主客观二分类任务，通过词性标注等预处理、微调情感Transformer模型、自定义加权分类器及数据翻译等方法，在CheckThat! 2024的多语言及单语言评测中取得了优异排名。
摘要：
本研究针对一项二分类任务，判断文本序列（句子或段落）是主观的还是客观的。该任务涵盖阿拉伯语、保加利亚语、英语、德语、意大利语五种语言以及一个多语言类别。我们的方法采用了多项关键技术。首先，我们通过词性（POS）标注、问号识别和注意力掩码应用对数据进行预处理。我们在数据集上微调了基于情感的Transformer模型MarieAngeA13/Sentiment-Analysis-BERT。鉴于客观数据更多导致的不平衡问题，我们实现了一个自定义分类器，为客观数据分配更高的权重。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结果：在多语言数据集（Macro F1=0.7121）和德语（Macro F1=0.7908）上获得最高分；在阿拉伯语（Macro F1=0.4908）和保加利亚语（Macro F1=0.7169）上排名第二；在意大利语（Macro F1=0.7430）上排名第三；在英语（Macro F1=0.6893）上排名第九。
英文摘要：
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).
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。
