论文标题
使用多模式的深层合奏来检测可恨的模因
Detecting Hateful Memes Using a Multimodal Deep Ensemble
论文作者
论文摘要
尽管已经使用机器学习算法取得了重大进展来检测仇恨言论，但仍然需要解决重要的技术挑战，以使其绩效更接近人类的准确性。我们研究了几种最新的视觉语言变压器架构，并提出改进以提高其在此任务的绩效。提出的模型的表现优于基准的幅度很大，在3,100多个参与者中排行榜上排名第5 $^{th} $。
While significant progress has been made using machine learning algorithms to detect hate speech, important technical challenges still remain to be solved in order to bring their performance closer to human accuracy. We investigate several of the most recent visual-linguistic Transformer architectures and propose improvements to increase their performance for this task. The proposed model outperforms the baselines by a large margin and ranks 5$^{th}$ on the leaderboard out of 3,100+ participants.
