Academic

Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling

arXiv:2603.02267v1 Announce Type: new Abstract: Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during the testing stage, so they may not provide effective supervision signals, leading to misclassification. To address this issue, we propose a \textbf{L}abel-guided \textbf{D}istance \textbf{S}caling (LDS) strategy. The core of our method is exploiting label semantics as supervision signals in both the training and testing stages. Specifically, in the training stage, we design a label-guided loss to inject label semantic information, pulling closer the sample representations and corresponding label representations. In the testing stage, we propose a Label-guided Scaler which scales sample representations with label semantics to provide additional supervision signals. Thus, even if l

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Yunlong Gao, Xinyue Liu, Yingbo Wang, Linlin Zong, Bo Xu · · 1 min read · 12 views
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arXiv:2603.02267v1 Announce Type: new Abstract: Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during the testing stage, so they may not provide effective supervision signals, leading to misclassification. To address this issue, we propose a \textbf{L}abel-guided \textbf{D}istance \textbf{S}caling (LDS) strategy. The core of our method is exploiting label semantics as supervision signals in both the training and testing stages. Specifically, in the training stage, we design a label-guided loss to inject label semantic information, pulling closer the sample representations and corresponding label representations. In the testing stage, we propose a Label-guided Scaler which scales sample representations with label semantics to provide additional supervision signals. Thus, even if labeled sample representations are far from class centers, our Label-guided Scaler pulls them closer to their class centers, thereby mitigating the misclassification. We combine two common meta-learners to verify the effectiveness of the method. Extensive experimental results demonstrate that our approach significantly outperforms state-of-the-art models. All datasets and codes are available at https://anonymous.4open.science/r/Label-guided-Text-Classification.

Executive Summary

The article proposes a novel approach to few-shot text classification, known as Label-guided Distance Scaling (LDS), which exploits label semantics as supervision signals in both training and testing stages. In the training stage, the method incorporates a label-guided loss to inject label semantic information, while in the testing stage, a Label-guided Scaler scales sample representations with label semantics to provide additional supervision signals. The authors combine two common meta-learners to verify the effectiveness of the method, demonstrating significant performance improvements over state-of-the-art models on various datasets. The proposed approach addresses the issue of misclassification due to randomly selected labeled samples during testing, providing a more robust and effective solution for few-shot text classification.

Key Points

  • ▸ The proposed Label-guided Distance Scaling (LDS) approach exploits label semantics as supervision signals in both training and testing stages.
  • ▸ The method incorporates a label-guided loss in the training stage to inject label semantic information.
  • ▸ A Label-guided Scaler is proposed in the testing stage to scale sample representations with label semantics and provide additional supervision signals.

Merits

Strength

The LDS approach effectively addresses the issue of misclassification due to randomly selected labeled samples during testing, providing a more robust and effective solution for few-shot text classification.

Improved Performance

The proposed method demonstrates significant performance improvements over state-of-the-art models on various datasets, showcasing its effectiveness in few-shot text classification.

Demerits

Limitation

The proposed approach may require additional computational resources to compute label semantics, which could be a limitation for large-scale datasets or resource-constrained environments.

Dependence on Label Semantics

The effectiveness of the LDS approach relies on the quality and accuracy of the label semantics, which may not always be available or reliable, particularly for complex or nuanced text classification tasks.

Expert Commentary

The proposed LDS approach is a significant contribution to the field of few-shot text classification, providing a novel and effective solution to address the issue of misclassification due to randomly selected labeled samples during testing. The approach builds upon existing meta-learning methods and relies on text representation learning to effectively scale sample representations with label semantics. While there are limitations, such as dependence on label semantics and potential computational requirements, the proposed approach has the potential to inform the development of more effective and robust text classification systems. As such, it is essential to continue exploring and refining this approach to further advance the field.

Recommendations

  • ✓ Further research is needed to investigate the potential applications of the LDS approach in various text classification tasks and policy domains.
  • ✓ The development of more efficient and scalable methods to compute label semantics is necessary to ensure the practicality and feasibility of the proposed approach.

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