Informed Graph Convolution Networks for Multilingual Short Text Understanding.

In the open domain environment, the state-of-the-art models cannot process analyze insufficient training data correctly. We propose an adaptive graph convolution network with informed machine learning for multilingual short text understanding to tackle these problems. Specifically, The prior knowledge to guide the graph neural network to extract sentence topics. We construct category anchor words as prior category keywords, prior category keywords and training data as independent information sources, and prior knowledge participates in the training of graph neural network. Moreover, we integrate the attention mechanism in the training process, so that the model can pay attention to task-related information adaptively. We explain the build blocks and present the integrated knowledge representation. The experimental results on the Multilingual Short Text (MST), THUCNews and AGNews datasets show that our method outperforms most of the existing methods. [ABSTRACT FROM AUTHOR]