EMNLP 2021main34 citations
Learning Prototype Representations Across Few-Shot Tasks for Event Detection
Viet Lai, Franck Dernoncourt, Thien Huu Nguyen
Abstract
We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/laiviet/fsl-proact.
BibTeX
@inproceedings{lai-etal-2021-learning,
title = "Learning Prototype Representations Across Few-Shot Tasks for Event Detection",
author = "Lai, Viet and
Dernoncourt, Franck and
Nguyen, Thien Huu",
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.427/",
doi = "10.18653/v1/2021.emnlp-main.427",
pages = "5270--5277"
}