| Challenge: | In hierarchical multi-label classification, samples are classified into one or multiple class labels organized in a structured label hierarchy. |
| Approach: | They apply and compare shallow capsule networks for hierarchical multi-label text classification and introduce a new real-world scenario dataset. |
| Outcome: | The proposed model outperforms neural networks and non-neural network architectures on a real-world scenario dataset. |
Similar Papers
Hyperbolic Capsule Networks for Multi-Label Classification (2020.acl-main)
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| Challenge: | Existing methods for classification of labels are limited by feature aggregation and encoding. |
| Approach: | They propose to use hyperbolic capsule networks to capture fine-grained label information . they also propose a new routing method to adaptively adjust capsule number during routing . |
| Outcome: | The proposed method significantly improves the performance of multi-label classification on tail labels. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization (D18-1)
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| Challenge: | Existing methods for categorization of short texts use non-hierarchical flat model, but they are limited by domain-independent knowledge distribution. |
| Approach: | They propose a method which leverages hierarchical relationships between pre-defined categories to tackle the data sparsity problem. |
| Outcome: | The proposed method is competitive with the state-of-the-art methods on a multi-label categorization task for short texts using two benchmark datasets. |
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
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| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two benchmark datasets. |
Exploiting Global and Local Hierarchies for Hierarchical Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods encode label hierarchy in a global view, which makes them hard to exploit hierarchical information. |
| Approach: | They propose to leverage label hierarchy in multi-label text classification by encoding label hierarchy as a static hierarchical structure containing all labels. |
| Outcome: | The proposed method achieves significant improvement on three benchmark datasets compared with the state-of-the-art method HGCLR. |
Hierarchical Transfer Learning for Multi-label Text Classification (P19-1)
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| Challenge: | Multi-Label Hierarchical Text Classification (MLHTC) is a task of categorizing documents into one or more topics organized in an hierarchical taxonomy. |
| Approach: | They propose a transfer learning based strategy where binary classifiers at lower levels are initialized using parameters of the parent classifier and fine-tuned on the child category classification task. |
| Outcome: | The proposed method improves on micro-F1 and macro-F1, respectively, compared to binary classifiers trained from scratch. |
Hierarchy-Aware Global Model for Hierarchical Text Classification (2020.acl-main)
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| Challenge: | Existing methods for hierarchical text classification are limited and lack holistic structural information. |
| Approach: | They propose a hierarchy-aware global model with two variants that learn hierarchy-based label embeddings through an encoder and conduct inductive fusion of label-alike text features. |
| Outcome: | The proposed model improves on three benchmark datasets. |
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations (2021.emnlp-main)
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| Challenge: | Existing approaches to hierarchical multi-label text classification ignore vertical category correlations or exploit dependencies across levels without considering horizontal correlations . |
| Approach: | They propose a hierarchical multi-label text classification framework that considers both vertical and horizontal category correlations. |
| Outcome: | The proposed framework improves on real-world HMTC datasets with significant improvements over baselines. |
MCapsNet: Capsule Network for Text with Multi-Task Learning (D18-1)
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| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation (D19-1)
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| Challenge: | Context modeling is essential to generate coherent and consistent translation for document-level Neural Machine Translations. |
| Approach: | They propose a query-guided capsule network to cluster context information into different perspectives from which the target translation may concern. |
| Outcome: | The proposed model outperforms baseline models on multiple datasets of different domains. |