Problems and Countermeasures of Intelligent Elderly Care Service in the Context of Fewer Children in China

With the intensification of population aging and the implementation of the three-child policy, the elderly care pressure of Chinese families continues to rise. Therefore, accelerating the construction of a new intelligent elderly care service model is an important measure to actively respond to population aging, ease the burden of family elderly care and promote high-quality economic development. In view of this, this study analyzed the intelligent elderly care service to explore the relevant countermeasures of the intelligent elderly care service in the context of fewer children

Problems and Countermeasures of Intelligent Elderly Care Service in the Context of Fewer Children in China

With the intensification of population aging and the implementation of the three-child policy, the elderly care pressure of Chinese families continues to rise. Therefore, accelerating the construction of a new intelligent elderly care service model is an important measure to actively respond to population aging, ease the burden of family elderly care and promote high-quality economic development. In view of this, this study analyzed the intelligent elderly care service to explore the relevant countermeasures of the intelligent elderly care service in the context of fewer children

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. Specifically, we extract evidence from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain texts. We construct graphs for both sources to obtain the relational structures of evidence. Based on these graphs, we propose a graph-based approach consisting of a graph-based contextual word representation learning module and a graph-based inference module. The first module utilizes graph structural information to re-define the distance between words for learning better contextual word representations. The second module adopts graph convolutional network to encode neighbor information into the representations of nodes, and aggregates evidence with graph attention mechanism for predicting the final answer. Experimental results on CommonsenseQA dataset illustrate that our graph-based approach over both knowledge sources brings improvement over strong baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the CommonsenseQA dataset