@inproceedings{hong-jang-2022-amal,
title = "{AMAL}: Meta Knowledge-Driven Few-Shot Adapter Learning",
author = "Hong, S. K. and
Jang, Tae Young",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.709",
doi = "10.18653/v1/2022.emnlp-main.709",
pages = "10381--10389",
abstract = "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.",
}
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%0 Conference Proceedings
%T AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning
%A Hong, S. K.
%A Jang, Tae Young
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F hong-jang-2022-amal
%X 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.
%R 10.18653/v1/2022.emnlp-main.709
%U https://aclanthology.org/2022.emnlp-main.709
%U https://doi.org/10.18653/v1/2022.emnlp-main.709
%P 10381-10389
Markdown (Informal)
[AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning](https://aclanthology.org/2022.emnlp-main.709) (Hong & Jang, EMNLP 2022)
ACL
- S. K. Hong and Tae Young Jang. 2022. AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10381–10389, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.