用双曲空间提升多跳问答，效果优于传统欧氏空间
Multi-Hop Reasoning for Question Answering with Hyperbolic Representations
- 将双曲表示融入编码器-解码器模型，对比其与欧氏空间的推理能力
- 在多个数据集上，双曲空间表现均更优，尤其在层次结构明显时优势显著
- 可学习的曲率初始化比随机初始化更好，且对层次化数据适应性强
双曲表示在建模知识图谱数据方面表现出色，而知识图谱常用于支持多跳推理。然而，针对该任务中双曲空间与欧氏空间的系统性对比仍不充分。本文通过简单集成双曲表示与编码器-解码器模型，开展了一组受控且全面的实验，比较了双曲空间与欧氏空间在多跳推理中的能力。结果表明，双曲空间在多样化的数据集上始终优于欧氏空间。此外，通过消融研究发现，以数据的δ双曲度为初始值的可学习曲率，性能优于随机初始化。进一步研究表明，当数据具有更强层次结构时，双曲表示的优势更为明显。
原文摘要 · Abstract (English)
Hyperbolic representations are effective in modeling knowledge graph data which is prevalently used to facilitate multi-hop reasoning. However, a rigorous and detailed comparison of the two spaces for this task is lacking. In this paper, through a simple integration of hyperbolic representations with an encoder-decoder model, we perform a controlled and comprehensive set of experiments to compare the capacity of hyperbolic space versus Euclidean space in multi-hop reasoning. Our results show that the former consistently outperforms the latter across a diverse set of datasets. In addition, through an ablation study, we show that a learnable curvature initialized with the delta hyperbolicity of the utilized data yields superior results to random initializations. Furthermore, our findings suggest that hyperbolic representations can be significantly more advantageous when the datasets exhibit a more hierarchical structure.
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