Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagarlapudi
Abstract
We consider the problem of multi-label classification, where the labels lie on a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is known. Encouraged by the recent success of hyperbolic embeddings in capturing hierarchical relations, we propose to jointly learn the classifier parameters as well as the label embeddings. Such a joint learning is expected to provide a twofold advantage: i) the classifier generalises better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy. We propose a novel formulation for the joint learning and empirically evaluate its efficacy. The results show that the joint learning improves over the baseline that employs label co-occurrence based pre-trained hyperbolic embeddings. Moreover, the proposed classifiers achieve state-of-the-art generalization on standard benchmarks. We also present evaluation of the hyperbolic embeddings obtained by joint learning and show that they represent the hierarchy more accurately than the other alternatives.- Anthology ID:
- 2021.eacl-main.247
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2829–2841
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.247
- DOI:
- 10.18653/v1/2021.eacl-main.247
- Cite (ACL):
- Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, and Saketha Nath Jagarlapudi. 2021. Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2829–2841, Online. Association for Computational Linguistics.
- Cite (Informal):
- Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (Chatterjee et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.eacl-main.247.pdf
- Code
- soumyac1999/hyperbolic-label-emb-for-hmc
- Data
- New York Times Annotated Corpus, RCV1