论文标题
将基于计数的特征纳入预先训练的模型，以改善立场检测
Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection
论文作者
论文摘要
社交媒体的爆炸性增长和普及彻底改变了我们交流和协作的方式。不幸的是，同样的访问和共享信息的便利性也导致了错误信息和宣传的爆炸。鉴于立场检测可以极大地帮助准确的预测，这项工作着重于提高自动立场检测，就像其他几个任务一样，预训练模型非常成功的任务非常成功。这项工作表明，立场检测的任务可以从基于功能的信息中受益，尤其是在某些表演类中的某些方面，但是，将这些功能整合到使用结合的预训练模型中是具有挑战性的。我们提出了一种新颖的体系结构，用于将功能与预先训练的模型集成在一起，以解决这些挑战并在Rumoureval 2019数据集中测试我们的方法。该方法在测试集中以63.94的F1得分实现了最新结果。
The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate. Unfortunately, this same ease of accessing and sharing information has led to an explosion of misinformation and propaganda. Given that stance detection can significantly aid in veracity prediction, this work focuses on boosting automated stance detection, a task on which pre-trained models have been extremely successful on, as on several other tasks. This work shows that the task of stance detection can benefit from feature based information, especially on certain under performing classes, however, integrating such features into pre-trained models using ensembling is challenging. We propose a novel architecture for integrating features with pre-trained models that address these challenges and test our method on the RumourEval 2019 dataset. This method achieves state-of-the-art results with an F1-score of 63.94 on the test set.
