Few-Shot Event Detection with Prototypical Amortized Conditional Random Field

This code is for Findings of ACL 2021 paper "Few-Shot Event Detection with Prototypical Amortized Conditional Random Field".

Overview

In this work, we present PA-CRF to solve the few-shot event detection task. It models the label dependency in the few-shot scenario by approximating the transition scores between labels based on the label prototypes. Gaussian distribution is introduced to alleviate the uncertain estimation resulting from insufficient data. Experimental results show that our PA-CRF achieves the best results on the benchmark dataset FewEvent.

You can find the paper here.

Requirements

python=3.7
pytorch=1.5.1
cuda=10.2
transformers=2.8.0

NOTE: Different versions of packages (such as pytorch, transformers, etc.) may lead to different results from the paper. However, the trend should still hold no matter what versions of packages you use.

Usage

1. Training model

python main.py --model=pa_crf --trainN=5 --evalN=5 --K=5 --Q=1

After training, this script will evaluate the model automatically. The best model will be saved in checkpoint directory.

All hyper-parameters are listed in config.py file. You can change it to conduct more experiments.

2. Evaluation

python main.py --model=pa_crf --trainN=5 --evalN=5 --K=5 --Q=1 --load_ckpt="your_checkpoint_name_saved_in_checkpoint_dir" --test

Citation

@inproceedings{cong2021PACRF,
 author = {Cong, Xin and Cui, Shiyao and Yu, Bowen and Liu, Tingwen and Wang, Yubin and Wang, Bin},
 booktitle = {Proc. of Findings of ACL},
 title = {Few-Shot Event Detection with Prototypical Amortized Conditional Random Field},
 year = {2021}
}