Label Augmentation for Zero-Shot Hierarchical Text Classification (2024.acl-long)
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| Challenge: | Hierarchical Text Classification is a difficult problem due to the lack of labeled data and the cost of manually annotating data samples. |
| Approach: | They propose a method that uses a Large Language Model to augment the deepest layer of the labels hierarchy to enhance its specificity. |
| Outcome: | The proposed method achieves state-of-the-art on four public datasets and a strong correlation between the metric values and the classification performance. |
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| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
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The Benefits of Label-Description Training for Zero-Shot Text Classification (2023.emnlp-main)
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| Challenge: | Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data to classify among specific label sets in downstream tasks. |
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Integrating Semantic Knowledge to Tackle Zero-shot Text Classification (N19-1)
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| Challenge: | Existing approaches to classify text documents of emerging classes are ineffective because of insufficient or even unavailable training data. |
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An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels (2020.emnlp-main)
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Ilias Chalkidis, Manos Fergadiotis, Sotiris Kotitsas, Prodromos Malakasiotis, Nikolaos Aletras, Ion Androutsopoulos
| Challenge: | Large-scale Multi-label Text Classification (LMTC) is a type of classification that assigns labels to a large set of labels. |
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Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
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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. |
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Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs (2020.emnlp-main)
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| Challenge: | Few/zero-shot learning is a big challenge of many classification tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. |
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Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Existing approaches to text classification assume a fixed set of labels . however, in real-world applications, there exists an infinite label space for describing a given text . |
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Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of hierarchical information. |
| Approach: | They propose a hierarchy-aware label semantics matching network to model the semantic relationship between text and labels in a semantic matching problem. |
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