KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.

Attention based Broadly Self-guided Network for Low light Image Enhancement

During the past years,deep convolutional neural networks have achieved impressive success in low-light Image Enhancement.Existing deep learning methods mostly enhance the ability of feature extraction by stacking network structures and deepening the depth of the network.which causes more runtime cost on single image.In order to reduce inference time while fully extracting local features and global features.Inspired by SGN,we propose a Attention based Broadly self-guided network (ABSGN) for real world low-light image Enhancement.such a broadly strategy is able to handle the noise at different exposures.The proposed network is validated by many mainstream benchmark.Additional experimental results show that the proposed network outperforms most of state-of-the-art low-light image Enhancement solutions.

Encoding Heterogeneous Social and Political Context for Entity Stance Prediction

Political stance detection has become an important task due to the increasingly polarized political ideologies. Most existing works focus on identifying perspectives in news articles or social media posts, while social entities, such as individuals and organizations, produce these texts and actually take stances. In this paper, we propose the novel task of entity stance prediction, which aims to predict entities' stances given their social and political context. Specifically, we retrieve facts from Wikipedia about social entities regarding contemporary U.S. politics. We then annotate social entities' stances towards political ideologies with the help of domain experts. After defining the task of entity stance prediction, we propose a graph-based solution, which constructs a heterogeneous information network from collected facts and adopts gated relational graph convolutional networks for representation learning. Our model is then trained with a combination of supervised, self-supervised and unsupervised loss functions, which are motivated by multiple social and political phenomenons. We conduct extensive experiments to compare our method with existing text and graph analysis baselines. Our model achieves highest stance detection accuracy and yields inspiring insights regarding social entity stances. We further conduct ablation study and parameter analysis to study the mechanism and effectiveness of our proposed approach.

Knowledge Graph Augmented Political Perspective Detection in News Media

Identifying political perspective in news media has become an important task due to the rapid growth of political commentary and the increasingly polarized ideologies. Previous approaches only focus on leveraging the semantic information and leaves out the rich social and political context that helps individuals understand political stances. In this paper, we propose a perspective detection method that incorporates external knowledge of real-world politics. Specifically, we construct a contemporary political knowledge graph with 1,071 entities and 10,703 triples. We then build a heterogeneous information network for each news document that jointly models article semantics and external knowledge in knowledge graphs. Finally, we apply gated relational graph convolutional networks and conduct political perspective detection as graph-level classification. Extensive experiments show that our method achieves the best performance and outperforms state-of-the-art methods by 5.49%. Numerous ablation studies further bear out the necessity of external knowledge and the effectiveness of our graph-based approach.