NLP has advanced greatly together with the proliferation of Transformer-based pre-trained language models. To adapt to a downstream task, the pre-trained language models need to be fine-tuned with a sufficient supply of annotated examples. In recent years, Adapter-based fine-tuning methods have expanded the applicability of pre-trained language models by substantially lowering the required amount of annotated examples. However, existing Adapter-based methods still fail to yield meaningful results in the few-shot regime where only a few annotated examples are provided. In this study, we present a meta-learning-driven low-rank adapter pooling method, called AMAL, for leveraging pre-trained language models even with just a few data points. We evaluate our method on five text classification benchmark datasets. The results show that AMAL significantly outperforms previous few-shot learning methods and achieves a new state-of-the-art.
CITATION STYLE
Hong, S. K., & Jang, T. Y. (2022). AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 (pp. 10381–10389). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.emnlp-main.709
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