Incremental Prompting

Introduction

This is the official repository for the paper "Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection" (COLING'22). More details on code will be released soon.

Basic Requirements

• Please make sure you have installed the following packages in your environment:

transformers==4.18.0
torch==1.7.1
torchmeta==1.8.0
numpy==1.19.5
tqdm==4.62.3

• You can install the requirements via running:

pip install -r requirements.txt

Data Preparation

• We use the ACE and MAVEN datasets for evaluation. Please note that ACE is not publicly released and requires a license to access.
• First download the dataset files under the following directory with specified file names:

./data/{DATASET_NAME}/{DATASET_SPLIT}.jsonl

• Here DATASET_NAME = {MAVEN, ACE}, DATASET_SPLIT = {train, dev, test}. Please make sure you have downloaded the files on all three splits. Also note that you need to preprocess the ACE dataset into the same format as MAVEN.
• Then run the follow script to preprocess the datasets:

python prepare_inputs.py

The script will generate preprocessed files under the corresponding dataset directory.

Training & Evaluation

• First create a directory./logs/ which will stored the model checkpoints, and ./log/ which will stored log files.
• Run the following script to start training. The script will also periodically evaluate the model on dev and test set.

python run.py

Reference

Please consider citing our paper if find it useful or interesting.

@inproceedings{liu2022incremental,
    title={Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection},
    author={Liu, Minqian and Chang, Shiyu and Huang, Lifu},
    booktitle={Proceedings of the 29th International Conference On Computational Linguistics},  
    year={2022}
}

Acknowledgement

Parts of the code in this repository are adopted from the work Lifelong Event Detection with Knowledge Transfer. We thank Zhiyang Xu for constructive comments to this work.