Leveraging Text-to-Text Transformers as Classifier Chain for Few-Shot Multi-Label Classification (2025.emnlp-main)
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| Challenge: | Multilabel text classification (MLTC) is an essential task in NLP applications. |
| Approach: | They propose a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning. |
| Outcome: | The proposed model outperforms baselines of similar size on three few-shot tasks. |
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| Challenge: | Existing methods for few-shot text classification ignore the semantic relevance of labels and are difficult to train because of the lack of training examples. |
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| Challenge: | Recent studies have shown that few-shot text classification is a poor solution for training data-intensive tasks. |
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From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)
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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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| Challenge: | despite the strong trend in NLP to explore the use of large language models, there is still limited work evaluating prompting and decoding mechanisms for SL tasks. |
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Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (D18-1)
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| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
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Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Myriam Tami, Victor Storchan, Ismail Ayed, Pablo Piantanida
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| Challenge: | Existing noisy multi-label text classification methods rely on the class-conditional noise assumption, but in practice, noisy labels exhibit a certain degree of correlation with the true labels. |
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Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification (2021.findings-acl)
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| Challenge: | Existing studies focus on building a meta-learner from input text but ignore abundant semantic information beneath class labels. |
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