QU-NLP 参与 CheckThat! 2025：采用带序列跨语言微调的特征增强Transformer模型检测新闻文章中的多语言主观性
QU-NLP at CheckThat! 2025: Multilingual Subjectivity in News Articles Detection using Feature-Augmented Transformer Models with Sequential Cross-Lingual Fine-Tuning
AI总结：
本研究针对CheckThat! 2025新闻主观性检测任务，提出融合上下文嵌入与统计/语言特征的特征增强Transformer模型，通过序列跨语言微调在多语言、单语言及零样本设置下取得有竞争力的性能，并验证了特征组合与跨语言迁移的作用。
摘要：
本文介绍了我们针对CheckThat! 2025任务1（主观性检测）的方法，该任务要求系统区分新闻文章中的句子是表达作者的主观看法，还是对所报道主题呈现客观观点。我们提出了一种特征增强的Transformer架构，将预训练语言模型的上下文嵌入与统计和语言特征相结合。我们的系统利用了带有额外词汇特征的预训练Transformer：针对阿拉伯语，我们使用了增强了词性（POS）标签和TF-IDF特征的AraELECTRA；针对其他语言，我们微调了跨语言DeBERTa V3模型，并通过门控机制将其与TF-IDF特征相结合。我们在单语言、多语言和零样本设置下，对包括英语、阿拉伯语、德语、意大利语以及多种未见过的语言在内的多种语言评估了系统。结果证明了我们方法的有效性，在不同语言上均取得了有竞争力的性能：在单语言设置下，英语表现突出（排名第1，macro-F1=0.8052）；德语排名第3（macro-F1=0.8013）；阿拉伯语排名第4（macro-F1=0.5771）；在零样本设置下，罗马尼亚语排名第1（macro-F1=0.8126）。我们还进行了消融分析，证明了将TF-IDF特征与门控机制相结合以及跨语言迁移对于主观性检测的重要性。此外，我们的分析揭示了模型对跨语言微调的顺序以及训练语言的语言邻近性均具有敏感性。
英文摘要：
This paper presents our approach to the CheckThat! 2025 Task 1 on subjectivity detection, where systems are challenged to distinguish whether a sentence from a news article expresses the subjective view of the author or presents an objective view on the covered topic. We propose a feature-augmented transformer architecture that combines contextual embeddings from pre-trained language models with statistical and linguistic features. Our system leveraged pre-trained transformers with additional lexical features: for Arabic we used AraELECTRA augmented with part-of-speech (POS) tags and TF-IDF features, while for the other languages we fine-tuned a cross-lingual DeBERTa~V3 model combined with TF-IDF features through a gating mechanism. We evaluated our system in monolingual, multilingual, and zero-shot settings across multiple languages including English, Arabic, German, Italian, and several unseen languages. The results demonstrate the effectiveness of our approach, achieving competitive performance across different languages with notable success in the monolingual setting for English (rank 1st with macro-F1=0.8052), German (rank 3rd with macro-F1=0.8013), Arabic (rank 4th with macro-F1=0.5771), and Romanian (rank 1st with macro-F1=0.8126) in the zero-shot setting. We also conducted an ablation analysis that demonstrated the importance of combining TF-IDF features with the gating mechanism and the cross-lingual transfer for subjectivity detection. Furthermore, our analysis reveals the model's sensitivity to both the order of cross-lingual fine-tuning and the linguistic proximity of the training languages.
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