论文标题
订单是不需要的：人格检测的动态深图卷积网络
Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection
论文作者
论文摘要
在社交网络分析等许多领域中，基于在线帖子的人格特质已经成为一项重要任务。此任务的挑战之一是将各个帖子的信息组装成每个用户的整体配置文件。虽然许多以前的解决方案只是将帖子串联到长文档中，然后通过顺序或分层模型编码文档，但它们引入了帖子的无根据订单，这可能会误导模型。在本文中，我们提出了一个动态的深图卷积网络（D-DGCN），以克服上述限制。具体而言，我们设计了一种学习到连接的方法，该方法采用动态多跳结构而不是确定性结构，并将其与DGCN模块结合使用，以自动学习帖子之间的连接。邮政编码器，学习到连接和DGCN的模块以端到端的方式共同培训。 Kaggle和Pandora数据集的实验结果表明，D-DGCN的性能优于最先进的基线。我们的代码可在https://github.com/djz233/d-dgcn上找到。
Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis. One of the challenges of this task is assembling information from various posts into an overall profile for each user. While many previous solutions simply concatenate the posts into a long document and then encode the document by sequential or hierarchical models, they introduce unwarranted orders for the posts, which may mislead the models. In this paper, we propose a dynamic deep graph convolutional network (D-DGCN) to overcome the above limitation. Specifically, we design a learn-to-connect approach that adopts a dynamic multi-hop structure instead of a deterministic structure, and combine it with a DGCN module to automatically learn the connections between posts. The modules of post encoder, learn-to-connect, and DGCN are jointly trained in an end-to-end manner. Experimental results on the Kaggle and Pandora datasets show the superior performance of D-DGCN to state-of-the-art baselines. Our code is available at https://github.com/djz233/D-DGCN.
