Prompt-Tuned Muti-Task Taxonomic Transformer (PTMTTaxoFormer) (2024.emnlp-industry)
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| Challenge: | Existing methods for Hierarchical Text Classification (HTC) are expensive and require explicit injection of the hierarchy, verbalizers, and/or prompt engineering. |
| Approach: | They propose a hierarchical text classification system that uses a single classifier to predict one or more topics using differentiable prompts and labels that are learnt through backpropagation. |
| Outcome: | The proposed model outperforms existing models on several benchmarks that span a range of topics consistently. |
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HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2022.emnlp-main)
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| Challenge: | Hierarchical text classification (HTC) is a multi-label classification problem with a complex label hierarchy. |
| Approach: | They propose a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label perspective using a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge. |
| Outcome: | The proposed method achieves state-of-the-art performance on 3 popular HTC datasets and is adept at handling imbalance and low resource situations. |
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |
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. |
Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification (2024.findings-acl)
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| Challenge: | Existing methods focus on hierarchy-aware text feature by exploiting explicit parent-child relationships, resulting in label confusion within each layer. |
| Approach: | They propose a dual-prompt tuning method which emphasizes discrimination among peer labels by performing contrastive learning on each hierarchical layer. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and is available on github. |
TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion (2023.emnlp-main)
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| Challenge: | Existing methods for automating taxonomy completion use subtasks to learn subtask results, ignoring the effects of subtask on the final prediction. |
| Approach: | They propose a multi-task automatic taxonomy completion method that attaches emerging concepts to an appropriate pair of hypernym and hyponym in existing taxonomies. |
| Outcome: | The proposed method improves on three datasets and improves inference efficiency. |
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. |
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)
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| Challenge: | Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. |
| Approach: | They propose a text-generation-based framework that uses language models to encode dynamic text representations. |
| Outcome: | The proposed framework surpasses existing methods while handling data and mitigating class imbalance. |
Reasoning for Hierarchical Text Classification: The Case of Patents (2026.findings-acl)
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| Challenge: | Hierarchical text classification (HTC) is one of the hardest HTC scenarios because of professional difficulties and extensive labels. |
| Approach: | They propose a framework that reformulates hierarchical classification as a step-by-step reasoning task. |
| Outcome: | The proposed framework outperforms supervised fine-tuning benchmarks on other widely used HTC benchmarks. |
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | Existing soft prompt methods focus on designing the input-independent prompts that steer the model to fit the domain of the new dataset. |
| Approach: | They propose a multi-level prompt tuning method that utilizes prompts at task-specific, domain-specific and context-specific levels to enhance the comprehension of input semantics. |
| Outcome: | The proposed method improves on 12 benchmarks on various QA formats and achieves an average improvement of 1.94% over the state-of-the-art methods. |
Developing Prefix-Tuning Models for Hierarchical Text Classification (2022.emnlp-industry)
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| Challenge: | Hierarchical text classification (HTC) is a key task in many industrial applications. Pre-trained Language Models (PLMs) have become dominant for most natural language processing (NLP) tasks. |
| Approach: | They investigate how prefix tuning can improve hierarchical text classification . prefix-tuning model only needs less than 1% of parameters to achieve performance . |
| Outcome: | The proposed model can achieve comparable performance to regular full fine-tuning. |