论文标题
用于分层多级分类的完全双曲线神经模型
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification
论文作者
论文摘要
用于细粒实体打字的标签清单的大小和复杂性已增长。尽管如此，它们表现出层次结构。双曲线空间为学习符号数据的分层表示提供了一种吸引人的方法。但是，尚不清楚如何将双曲线组件集成到下游任务中。这是提出了用于多级多标签分类的完全双曲线模型的第一项工作，该模型在双曲线空间中执行所有操作。我们在两个具有挑战性的数据集上评估了所提出的模型，并与在欧几里得假设下运行的不同基线进行比较。我们的双曲线模型从类别分布中渗透了潜在的层次结构，捕获库存中的隐式式siby依关系，并显示出对细粒度分类的最新方法的性能，并显着降低了参数大小。彻底的分析阐明了每个组件在最终预测中的影响，并展示了其与欧几里得层的易于整合。
Label inventories for fine-grained entity typing have grown in size and complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data. However, it is not clear how to integrate hyperbolic components into downstream tasks. This is the first work that proposes a fully hyperbolic model for multi-class multi-label classification, which performs all operations in hyperbolic space. We evaluate the proposed model on two challenging datasets and compare to different baselines that operate under Euclidean assumptions. Our hyperbolic model infers the latent hierarchy from the class distribution, captures implicit hyponymic relations in the inventory, and shows performance on par with state-of-the-art methods on fine-grained classification with remarkable reduction of the parameter size. A thorough analysis sheds light on the impact of each component in the final prediction and showcases its ease of integration with Euclidean layers.
