Core Concepts
Proposing HILL for HTC with information lossless contrastive learning.
Abstract
The content introduces HILL, a method for hierarchical text classification using contrastive learning. It addresses the limitations of existing self-supervised methods by focusing on information lossless contrastive learning. The paper discusses the structure of HILL, the theoretical framework, experimental results, ablation studies, and limitations.
Structure:
- Introduction to HILL
- Self-supervised methods in NLP
- Proposed method: HILL
- Theoretical framework
- Experimental results
- Ablation studies
- Limitations
Stats
Experiments on three datasets are conducted to verify the superiority of HILL.
The weight of contrastive loss λclr is set to 0.001, 0.1, 0.3 for WOS, RCV1, and NYTimes.
The optimal height K of coding trees is set to 3, 2, and 3 for the datasets.
Quotes
"Our model surpasses all supervised learning models and the contrastive learning model across all three datasets."
"The proposed HILL demonstrates average improvements of 1.85% and 3.38% on Micro-F1 and Macro-F1 compared to vanilla BERT."