Glaciohound/ACL2019-ODEE

This is the code for our ACL 2019 paper "Open Domain Event Extraction Using Neural Latent Variable Models"

Open Domain Event Extraction Using Neural Latent Variable Models (ODEE)

This is the python3 code for the paper "Open Domain Event Extraction Using Neural Latent Variable Models" in ACL 2019.

Prepare ELMo model

Modify the Line 24 and 25 in cache_features.py.

The fine-tune process need 2 * GTX 1080Ti, if the fine-tune process is costly or somehow failed to complete, please use the initial parameters in allennlp.

Using the "small" version of ElMo. https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x1024_128_2048cnn_1xhighway/elmo_2x1024_128_2048cnn_1xhighway_weights.hdf5 https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x1024_128_2048cnn_1xhighway/elmo_2x1024_128_2048cnn_1xhighway_options.json

Please note that it is optional to finetune the ELMo model if you just want to complete the whole procedure or use the model in somewhere else.

Prepare Data and Train Model

The data is HERE.

1. run the dataprocessor
2. run sudo chown [YOUR_UERS] [PROCESSED_DIR] and specify the directories in setting.yaml manually
3. run pip3 install -r requirements.txt to install required packages
4. run python3 cache_features.py
5. run python3 train_avitm.py 2>&1 | tee logs/train.log
6. run python3 generate_slot_topN.py 2>&1 | tee logs/generation.log
7. run python3 decode.py 2>&1 | tee logs/decoding.log
8. follow steps in slotcoherence/README.md to split the corpus
9. run cd slotcoherence && bash ./run-oc.sh 2>&1 | tee $SCRIPTPATH/logs/coherence.log && cd ..
10. run visualize_test.ipynb

Produced Data

1. *.json.pt: cached features of ODEE input
2. *.json.answer: decoded full results of a news group
3. *.json.template: decoded template of a news group
4. *.json.events.topN: decoded top-N events of a news group
5. *.json.labeled: labeled events of test split
6. slotcoherence/slot_head_words.txt: generated topN head words for each slot

Results

After about 80 epoches (2021-05-13):

Average Topic Coherence = 0.076
Median Topic Coherence = 0.075

After about 110 epoches (2021-05-17-4), loss ~ 4824:

Average Topic Coherence = 0.133
Median Topic Coherence = 0.119

After about 130 epoches (2021-05-17-2), loss ~ 4795:

Average Topic Coherence = 0.141
Median Topic Coherence = 0.155

After about 150 epoches (2021-05-17-3), loss ~ 4785:

Average Topic Coherence = 0.139
Median Topic Coherence = 0.126

After about 260 epoches (2021-05-14):

Average Topic Coherence = 0.109
Median Topic Coherence = 0.104

After about 460 epoches (2021-05-17), loss ~ 4752:

Average Topic Coherence = 0.074
Median Topic Coherence = 0.067

Cite

Please cite our ACL 2019 paper:

@inproceedings{DBLP:conf/acl/LiuHZ19,
  author    = {Xiao Liu and
               Heyan Huang and
               Yue Zhang},
  title     = {Open Domain Event Extraction Using Neural Latent Variable Models},
  booktitle = {Proceedings of the 57th Conference of the Association for Computational
               Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
               Volume 1: Long Papers},
  pages     = {2860--2871},
  year      = {2019},
  crossref  = {DBLP:conf/acl/2019-1},
  url       = {https://www.aclweb.org/anthology/P19-1276/},
  timestamp = {Wed, 31 Jul 2019 17:03:52 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/acl/LiuHZ19},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}