EMNLP 2021main156 citations
Improving and Simplifying Pattern Exploiting Training
Derek Tam, Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, Colin Raffel
Abstract
Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are very few labeled examples available for fine-tuning. Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning. However, PET uses task-specific unlabeled data. In this paper, we focus on few-shot learning without any unlabeled data and introduce ADAPET, which modifies PET’s objective to provide denser supervision during fine-tuning. As a result, ADAPET outperforms PET on SuperGLUE without any task-specific unlabeled data.
BibTeX
@inproceedings{tam-etal-2021-improving,
title = "Improving and Simplifying Pattern Exploiting Training",
author = "Tam, Derek and
R. Menon, Rakesh and
Bansal, Mohit and
Srivastava, Shashank and
Raffel, Colin",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.407/",
doi = "10.18653/v1/2021.emnlp-main.407",
pages = "4980--4991"
}