Papers by Qiaoyang Luo
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. |
| Approach: | They propose a framework to make full use of label semantics in few-shot text classification systems. |
| Outcome: | The proposed framework can be plugged into the existing few-shot text classification system. |
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot text classification suffer from overfitting due to the lack of matching between the few amount of samples and complicated models. |
| Approach: | They propose a method to improve model generalization ability to a new task by leveraging a meta-learner via gradient similarity method. |
| Outcome: | The proposed method improves few-shot text classification performance on several benchmarks. |