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Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification


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."

Key Insights Distilled From

by He Zhu,Junra... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17307.pdf
HILL

Deeper Inquiries

How does the height of the coding tree impact the performance of HILL on different datasets

The height of the coding tree in the HILL model has a significant impact on its performance across different datasets. As shown in the experimental results, the optimal height of the coding tree varies for each dataset. For example, in the case of the WOS dataset, the optimal height was found to be 3, while for RCV1-v2 it was 2, and for NYTimes it was 3. The performance of HILL sharply degrades as the height of the coding tree increases beyond the optimal value. This suggests that a higher coding tree height may lead to more explosive gradients or unnecessary complexity in the model, negatively affecting its performance. The optimal height of the coding tree seems to be more related to the volume of the label set rather than the height of the label hierarchies in the datasets.

What are the implications of the ablation studies on the necessity of the proposed methods in HILL

The ablation studies conducted on the HILL model provide valuable insights into the necessity of the proposed methods in the model. By removing specific components one at a time and evaluating the performance, the following implications were observed: Hierarchical Representation Learning: Replacing the structure encoder with commonly used graph neural networks resulted in inferior performance compared to the hierarchical representation learning module in HILL. This highlights the effectiveness of extracting syntactic information through structural entropy minimization and hierarchical representation learning. Contrastive Learning: Removing the contrastive learning component from the model led to a significant decrease in performance, emphasizing the importance of contrastive learning in HILL. The model without contrastive learning showed a notable decline in performance, indicating the necessity of this component for effective learning. Structural Entropy Minimization: Directly feeding the initial coding tree into the structure encoder resulted in decreased performance, indicating the importance of structural entropy minimization in constructing the optimal coding tree. The model without this component exhibited a performance drop, underscoring the significance of this step in the model. Overall, the ablation studies demonstrate that the hierarchical representation learning, contrastive learning, and structural entropy minimization components are essential for the effectiveness of the HILL model in hierarchical text classification.

How does the efficiency of HILL in terms of time and memory compare to other contrastive learning models like HGCLR

In terms of efficiency, HILL demonstrates notable advantages over other contrastive learning models like HGCLR, particularly in terms of time and memory usage. The comparison between HILL and HGCLR revealed the following: Number of Trainable Parameters: HILL has significantly fewer trainable parameters compared to HGCLR. On average, HILL has 7.34 million parameters, while HGCLR has 19.04 million parameters. This difference in parameter count indicates that HILL is more efficient in terms of parameter utilization. Training Time: HILL exhibits faster training speed compared to HGCLR. The average training time per epoch for HILL is 789.2 seconds, which is approximately half the time taken by HGCLR (1504.7 seconds). This indicates that HILL is more time-efficient in training the model. Overall, the efficiency analysis highlights that HILL is a time- and memory-saving model compared to HGCLR, making it a more efficient choice for hierarchical text classification tasks.
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