Neural Networks in a Product of Hyperbolic Spaces

Jun Takeuchi, Noriki Nishida, Hideki Nakayama


Abstract
Machine learning in hyperbolic spaces has attracted much attention in natural language processing and many other fields. In particular, Hyperbolic Neural Networks (HNNs) have improved a wide variety of tasks, from machine translation to knowledge graph embedding. Although some studies have reported the effectiveness of embedding into the product of multiple hyperbolic spaces, HNNs have mainly been constructed in a single hyperbolic space, and their extension to product spaces has not been sufficiently studied. Therefore, we propose a novel method to extend a given HNN in a single space to a product of hyperbolic spaces. We apply our method to Hyperbolic Graph Convolutional Networks (HGCNs), extending several HNNs. Our model improved the graph node classification accuracy especially on datasets with tree-like structures. The results suggest that neural networks in a product of hyperbolic spaces can be more effective than in a single space in representing structural data.
Anthology ID:
2022.naacl-srw.27
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Month:
July
Year:
2022
Address:
Hybrid: Seattle, Washington + Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
211–221
Language:
URL:
https://aclanthology.org/2022.naacl-srw.27
DOI:
10.18653/v1/2022.naacl-srw.27
Bibkey:
Cite (ACL):
Jun Takeuchi, Noriki Nishida, and Hideki Nakayama. 2022. Neural Networks in a Product of Hyperbolic Spaces. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 211–221, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
Cite (Informal):
Neural Networks in a Product of Hyperbolic Spaces (Takeuchi et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-srw.27.pdf
Video:
 https://aclanthology.org/2022.naacl-srw.27.mp4
Data
Pubmed