Papers with hierarchical text classification
Hierarchical Text Classification with Reinforced Label Assignment (D19-1)
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| Challenge: | Existing hierarchical text classification methods make local decisions regarding labels or ignore hierarchy information during inference. |
| Approach: | They propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. |
| Outcome: | The proposed method outperforms state-of-the-art methods on five datasets and four base models and achieves an average improvement of 33.4% over flat classifiers. |
Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification (2023.acl-long)
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| Challenge: | Existing work on the hierarchical text classification problem is limited due to the complexity of label hierarchy and intensive labeling cost. |
| Approach: | They propose a path-based few-shot setting and a strict path-basic evaluation metric to further explore few- shot HTC tasks. |
| Outcome: | The proposed framework outperforms those who inject hierarchy through graph encoders on three popular HTC datasets under the few-shot setting. |
Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks (2020.acl-main)
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| Challenge: | Hierarchical text classification is a complex task that requires extended training time and a large number of parameters. |
| Approach: | They propose a top-up-classification task using dictionaries and auxiliary task from external dictionary definitions. |
| Outcome: | The proposed method outperforms previous studies using a reduced number of parameters in two well-known English datasets. |
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization (2021.naacl-main)
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| Challenge: | Existing models for hierarchical text classification do not consider statistical constraint on label representations learned by structure encoder. |
| Approach: | They propose a new hierarchical text classification model called HTCInfoMax which incorporates two modules to improve the model's representations. |
| Outcome: | The proposed model can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information. |
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
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| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work. |
| Approach: | They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two widely used datasets. |
HYDRA: A Multi-Head Encoder-only Architecture for Hierarchical Text Classification (2025.emnlp-main)
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| Challenge: | State-of-the-art approaches rely on complex components like graph encoders, label semantics, and autoregressive decoders. |
| Approach: | They propose a multi-head encoder-only architecture for hierarchical text classification that treats each level as a separate classification task with its own label space. |
| Outcome: | The proposed architecture matches or exceeds state-of-the-art methods on four benchmarks. |
Ensembling Prompting Strategies for Zero-Shot Hierarchical Text Classification with Large Language Models (2025.emnlp-main)
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| Challenge: | Hierarchical text classification is a challenging task in natural language processing. |
| Approach: | They propose a method which integrates the results of diverse prompting strategies to promote LLMs’ reliability. |
| Outcome: | The proposed method boosts the performance of single prompting strategies and achieves SOTA results on three benchmark datasets. |
LGSA: Label Geometry Structuring and Aligning for Hierarchical Text Classification (2026.acl-long)
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| Challenge: | Existing hierarchical text classification methods use prompt tuning or contrastive learning to implicitly learn label embeddings for classification, but this method fails to model hierarchy-aware geometric relations among labels. |
| Approach: | They propose a two-stage framework that transforms the label hierarchy from an implicit prior into an explicit embedding by using a general orthogonal frame. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on three real-world HTC datasets. |
Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. |
| Approach: | They propose an explicit-agmented-generativ-e framework with distribution modification for hierarchical text classification. |
| Outcome: | The proposed framework improves on the initial distributions of tail classes and avoids misinterpreting predictions on unbalanced data. |