论文标题
Reintel Challenge 2020：一种用于检测越南SNS不可靠信息的多模式合奏模型
ReINTEL Challenge 2020: A Multimodal Ensemble Model for Detecting Unreliable Information on Vietnamese SNS
论文作者
论文摘要
在本文中，我们在VLSP 2020 Reintel Challenge上介绍了不可靠的信息识别任务的方法。任务是将一条信息分类为可靠或不可靠的类别。我们提出了一个新型的多模式集合模型，该模型结合了两个多模型来解决任务。在每个多模式模型中，我们合并了从三种不同数据类型中获取的特征表示：文本，图像和元数据。多模式特征来自三个神经网络，并融合进行分类。实验结果表明，我们提出的多模式集合模型在ROC AUC评分方面对单个模型进行了改进。我们在挑战的私人测试中获得了0.9445 AUC分数。
In this paper, we present our methods for unrealiable information identification task at VLSP 2020 ReINTEL Challenge. The task is to classify a piece of information into reliable or unreliable category. We propose a novel multimodal ensemble model which combines two multimodal models to solve the task. In each multimodal model, we combined feature representations acquired from three different data types: texts, images, and metadata. Multimodal features are derived from three neural networks and fused for classification. Experimental results showed that our proposed multimodal ensemble model improved against single models in term of ROC AUC score. We obtained 0.9445 AUC score on the private test of the challenge.
