Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation

A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template-based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two automatic metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.Comment: ACL 201

Disentangled Variational Auto-Encoder for Semi-supervised Learning

Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has demonstrated the benefits of semi-supervised learning. The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE. Given the limited labeled data, learning the parameters for the classifiers may not be an optimal solution for exploiting label information. Therefore, in this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via the equality constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.Comment: 6 figures, 10 pages, Information Sciences 201

Role of Symmetry in the Transport Properties of Graphene Nanoribbons under Bias

The intrinsic transport properties of zigzag graphene nanoribbons (ZGNRs) are investigated using first principles calculations. It is found that although all ZGNRs have similar metallic band structure, they show distinctly different transport behaviors under bias voltages, depending on whether they are mirror symmetric with respect to the midplane between two edges. Asymmetric ZGNRs behave as conventional conductors with linear current-voltage dependence, while symmetric ZGNRs exhibit unexpected very small currents with the presence of a conductance gap around the Fermi level. This difference is revealed to arise from different coupling between the conducting subbands around the Fermi level, which is dependent on the symmetry of the systems.Comment: 4 pages, 4 figure

Solution of Nonlinear Elliptic Boundary Value Problems and Its Iterative Construction

We study a kind of nonlinear elliptic boundary value problems with generalized p-Laplacian operator. The unique solution is proved to be existing and the relationship between this solution and the zero point of a suitably defined nonlinear maximal monotone operator is investigated. Moreover, an iterative scheme is constructed to be strongly convergent to the unique solution. The work done in this paper is meaningful since it combines the knowledge of ranges for nonlinear operators, zero point of nonlinear operators, iterative schemes, and boundary value problems together. Some new techniques of constructing appropriate operators and decomposing the equations are employed, which extend and complement some of the previous work

How Leaders Generate Meanings For Monetary Rewards

Scant research has focused on how to increase the value of monetary rewards when they are delivered by leaders to employees. Drawing upon the perspectives of sensegiving and sensemaking, this study explores how leaders generate meanings of monetary rewards perceived by employee recipients in organizational settings. Using a qualitative method design and analyzing qualitative data from 291 incidents, we found that in the distribution process of monetary rewards, sensemaking of employees included strong and weak instrumental meanings as well as symbolic meanings. The results show that leaders adopted a set of sensegiving strategies in distributing monetary rewards including emphasizing money gain/loss and utility, providing feedback, valuing employees, orienting toward the future, guiding values, and publicizing. In the presence of leader’s sensegiving, employee recipients endorsed more positive symbolic meanings of monetary rewards (i.e., recognition and respect). Our research offers a richer view of the role of leader’s sensegiving in making monetary rewards gain more value through employees’ sensemaking, and enriches understanding of monetary rewards, leadership, sensegiving and sensemaking