DocED
This repository is the official implementation of the ACL 2021 paper MLBiNet: A Cross-Sentence Collective Event Detection Network.
Requirements
To install basic requirements:
pip install requirements.txt
Datasets
ACE2005 can be found here: https://catalog.ldc.upenn.edu/LDC2006T06
Basic training
To evaluate a setting with serveral random trials, execute
python run_experiments_multi.py
Main hyperparameters in train_MLBiNet.py include:
--tagging_mechanism, mechanism to model event inter-dependency, you can choose one of "forward_decoder", "backward_decoder" or "bidirectional_decoder"
--num_tag_layers, number of tagging layers, 1 indicates that we do sentence-level ED, 2 indicates that information of adjacent sentences were aggregated, ...
--max_doc_len, maximum number of consecutive sentences are extracted as a mini-document, we can set it as 8 or 16
--tag_dim, dimension of an uni-directional event tagging vector
--self_att_not, whether to apply self-attention mechanism in sentence encoder
Main results
Overall performance on ACE2005
Performance on detecting multiple events collectively
where 1/1 means one sentence that has one event; otherwise, 1/n is used.
Performance of our proposed method with different multi-layer settings or decoder methods
How to Cite
@inproceedings{ACL2021_MLBiNet,
author = {Dongfang Lou and
Zhilin Liao and
Shumin Deng and
Ningyu Zhang and
Huajun Chen},
title = {MLBiNet: A Cross-Sentence Collective Event Detection Network},
booktitle = {{ACL}},
publisher = {Association for Computational Linguistics},
year = {2021}
}