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Incremental Prompting

Introduction

This is the official repository for the paper "Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection" (COLING'22).

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

• To run different task permutations, modify the perm-id argument in utils/options.py. The valid values are [0, 1, 2, 3, 4].

Reference

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

@inproceedings{liu-etal-2022-incremental,
    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",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.189",
    pages = "2157--2165",
}

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.