Network Representation Learning (NRL) has achieved remarkable success in learning low-dimensional representations for network nodes. However, most NRL methods, including Graph Neural Networks (GNNs) and their variants, face critical challenges. First, labeled network data, which are required for training most GNNs, are expensive to obtain. Second, existing methods are sub-optimal in preserving comprehensive topological information, including structural and positional information. Finally, most GNN approaches ignore the rich node content information. To address these challenges, we propose a self-supervised Network-to-Network framework (Net2Net) to learn semantically meaningful node representations. Our framework employs a pretext task of node position prediction (PosPredict) to effectively fuse the topological and content knowledge into low-dimensional embeddings for every node in a semi-supervised manner. Specifically, we regard a network as node content and position networks, where Net2Net aims to learn the mapping between them. We utilize a multi-layer recursively composable encoder to integrate the content and topological knowledge into the egocentric network node embeddings. Furthermore, we design a cross-modal decoder to map the egocentric node embeddings into their node position identities (PosIDs) in the node position network. Extensive experiments on eight diverse networks demonstrate the superiority of Net2Net over comparable methods.

Specifying an appropriate skill set for a job position is critical for talent recruitment. However, it is often more difficult than people think since it needs a great understanding of the role of the position, related technologies, and even the global situation of the job market. To this end, we propose to learn the mapping between the job position description and its required skills in a data-driven manner. This task is challenging due to the complex mapping relationships between job descriptions and skills, which is caused by complex influencing factors. In this paper, we propose a novel Multi-Graph Neural Network based Skill Prediction model (MGNSP) to make skill prediction by learning effective deep semantics matching of job positions and skills. Specifically, to capture the complex heterogeneous relations among the job positions, skills, and meta information, we devise a joint learning approach of graph neural networks for multiple information networks, which are J-Net, S-Net and JS-Net, respectively. After jointly learning complementary semantics of job positions and skills with three multi-layer graph neural networks from these information networks, the skills are predicted by learning to match their representations. Extensive experimental results on a real-world dataset validate the effectiveness of our model.

The interpretable charge prediction task is to predict the final charges according to the fact descriptions, at the same time generate the corresponding explanations. The task is often more difficult than people think, as the explanation and prediction are highly interdependent, which is ignored by existing methods. In this paper, we deal with the interpretable charge prediction task from the perspective of generating the prediction and explanation (court view) pair while considering their dependency. To this end, we propose a Joint Prediction and Generation Model, named JPGM, which includes a coarse-to-fine classifier and a keyword-aware generator. Specifically, firstly, the classifier predicts a group of charge labels given the fact description. Then, we select the most matchable keywords of every charge from the pre-defined charge-discriminative keywords by an attention mechanism. Furthermore, the generator generates explanation using both the fact description and the most matchable keywords. Finally, to refine the prediction, the generated explanation and fact description are fused for final charge prediction. The classifier and generator are trained under an alternating procedure, which alleviates the error propagation. The experimental results validate that our model can effectively address the dependency issue and predict the charge with more interpretability.

Opinion entity extraction is a fundamental task in fine-grained opinion mining.Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them.However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc.In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs.To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit.The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously.Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels.To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks.Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.

Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think.It is challenging to specify the level of education, experience, relevant skills per the company information and job description.To this end, we propose a novel task of Job Posting Generation (JPG) that is cast as a conditional text generation problem to generate job requirements according to the job descriptions.To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA.Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels.At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results.The proposed approach is evaluated on real-world job posting data.Experimental results clearly demonstrate the effectiveness of the proposed method 1 .