HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification (2305.15182v2)

Abstract: Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feasibility of a memory-friendly model with strong generalization capability that could boost the performance of HTC without prior statistics or label semantics. In this paper, we propose Hierarchy-aware Tree Isomorphism Network (HiTIN) to enhance the text representations with only syntactic information of the label hierarchy. Specifically, we convert the label hierarchy into an unweighted tree structure, termed coding tree, with the guidance of structural entropy. Then we design a structure encoder to incorporate hierarchy-aware information in the coding tree into text representations. Besides the text encoder, HiTIN only contains a few multi-layer perceptions and linear transformations, which greatly saves memory. We conduct experiments on three commonly used datasets and the results demonstrate that HiTIN could achieve better test performance and less memory consumption than state-of-the-art (SOTA) methods.

Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

The paper presents the Hierarchy-aware Tree Isomorphism Network (HiTIN), a novel approach addressing challenges in Hierarchical Text Classification (HTC), a subtask of multi-label classification. HTC is particularly challenging due to the hierarchical relationship among the labels, characterized by complex parent-child relationships. Existing dual-encoder models used in HTC are noted for their hefty memory usage and reliance on domain-specific knowledge, presenting limitations in generalization capability. HiTIN introduces a memory-efficient model that seeks to improve HTC performance without necessitating prior statistical knowledge or label semantics.

Methodology and Contributions

The primary innovation in HiTIN is the conversion of the label hierarchy into a syntactically guided tree structure called a "coding tree," achieved via structural entropy minimization. Unlike existing methods that depend heavily on graph neural networks and learned label representations, HiTIN primarily utilizes structural information. The coding tree enables effective hierarchy-aware encoding of the data without over-reliance on label semantics.

Key contributions include:

• Decoding Label Hierarchies: The coding tree structures are derived using structural entropy, reflecting the hierarchical organization of the original graph. This approach balances structural information across layers efficiently.
• Efficiency and Simplification: HiTIN comprises mainly a few multi-layer perceptions and linear transformations, marking a significant reduction in memory consumption compared to state-of-the-art models.
• Empirical Superiority: Experiments conducted on three benchmark datasets (WOS, RCV1-v2, and NYTimes) illustrate HiTIN's superior test performance and reduced memory footprint. The model shows improved Micro-F1 and Macro-F1 scores compared to competing methods.

Strong Numerical Results and Contradictory Claims

HiTIN delivers measurable improvements over state-of-the-art methods in HTC. When implemented with traditional TextRCNN or BERT encoders, HiTIN consistently achieves higher accuracy metrics (up to 3.55% improvement in Micro-F1 scores on TextRCNN setups) across varied datasets. The method's capacity to compress the hierarchical information efficiently without prior domain data challenges prevailing assumptions about the necessity of extensive domain knowledge for effective HTC.

Implications for Research and Practice

HiTIN's framework provides a new avenue for HTC tasks, leveraging structural transformations as a fundamental feature rather than additional knowledge graphs or deep semantic features. Practically, this translates to more efficient hierarchical models that can be deployed over diverse datasets without the typical preprocessing overhead. Theoretically, the paper set an intriguing precedent for applications of structural entropy minimization in other domains of language processing and information extraction.

Future Developments

The HiTIN approach opens up several potential lines of inquiry in AI research. Future work could explore the application of structural entropy in other machine learning paradigms, further optimizing the trade-off between computational efficiency and classification performance. Moreover, extending HiTIN's principles to real-time data processing and dynamic HTC environments could showcase its robustness beyond static corpora.

In summary, the paper presents a robust alternative to existing HTC models, focusing on leveraging structural hierarchy transformations to boost performance and efficiency. Its innovative use of structural entropy-guided coding trees over traditional encoder designs may inspire further exploration and cross-pollination of ideas in related fields.