A fully hyperbolic neural model for hierarchical multi-class classification

18Citations
Citations of this article
77Readers
Mendeley users who have this article in their library.

Abstract

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.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

López, F., & Strube, M. (2020). A fully hyperbolic neural model for hierarchical multi-class classification. In Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020 (pp. 460–475). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.findings-emnlp.42

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 22

73%

Researcher 6

20%

Lecturer / Post doc 2

7%

Readers' Discipline

Tooltip

Computer Science 26

74%

Linguistics 5

14%

Engineering 3

9%

Neuroscience 1

3%

Save time finding and organizing research with Mendeley

Sign up for free