论文标题
图形神经网络中的关系提取的通用和趋势感知的课程学习
Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks
论文作者
论文摘要
我们为图神经网络提供了一种通用和趋势感知的课程学习方法。它通过结合样本级别的损失趋势来扩展现有方法，从而更好地区分更轻松的样本，并安排它们进行培训。该模型有效地集成了文本和结构信息，以在文本图中提取关系提取。实验结果表明，该模型提供了对样本难度的强大估计，并显示了几个数据集对最新方法的显着改善。
We present a generic and trend-aware curriculum learning approach for graph neural networks. It extends existing approaches by incorporating sample-level loss trends to better discriminate easier from harder samples and schedule them for training. The model effectively integrates textual and structural information for relation extraction in text graphs. Experimental results show that the model provides robust estimations of sample difficulty and shows sizable improvement over the state-of-the-art approaches across several datasets.
