cltl/wsd-dynamic-sense-vector

A Deep Dive into Word Sense Disambiguation with LSTM

This package contains the code replicate the experiments from:

@InProceedings{C18-1030,
  author = 	"Le, Minh
		and Postma, Marten
		and Urbani, Jacopo
		and Vossen, Piek",
  title = 	"A Deep Dive into Word Sense Disambiguation with LSTM",
  booktitle = 	"Proceedings of the 27th International Conference on Computational Linguistics",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"354--365",
  location = 	"Santa Fe, New Mexico, USA",
  url = 	"http://aclweb.org/anthology/C18-1030"
}

Demo

For a demo, we refer to here

Setup

pip3 install --user beautifulsoup4 sklearn testfixtures unittest2 pyemd morfessor
pip3 install --user pandas==0.20.3 seaborn==0.8.1
pip3 install --user spacy
pip3 install --user https://github.com/explosion/spacy-models/releases/download/en_core_web_md-1.2.1/en_core_web_md-1.2.1.tar.gz
python3 -m spacy link en_core_web_md en_default
pip3 install --user nltk
printf 'import nltk; nltk.download("wordnet")' | python3
pip3 install --user tensorflow-gpu
pip3 install --user docopt

Put a link to your copy of Gigaword 5th edition

ln -s /path/to/gigaword/5ed data/gigaword

Install the modified version of gensim in order to train sense embeddings.

./install-gensim-modified.sh

Make sure you have a Java JDK installed.

Training models

The scripts you will find in the repo are meant to capture exactly what how we carried out our experiments, i.e., like a lab log. These experiments are very computing intensive -- we have run different parts of them in 3 different high-performance clusters -- so anyone trying to reproduce them likely needs to arrange their own supercomputer. As we don't have control over what computing environment people will use, we can't guarantee that the scripts can run as-is.

Requirements: You'll need at least 64 GB of RAM to run the preparation script.

You don't need access to Dutch DAS-5 or Cartesius to run these steps. The *.job files are bash script that you could run on any Unix machine.

Reproduce variation/stability experiments

These experiments measure how much the performance is affected by the randomness in training. Basically, we train smaller models many times, each time with a different (but fixed) random seed.

Pre-process GigaWord into plain text: git checkout 694cb4d && sbatch process-gigaword.job
More preprocessing to make binary files: git checkout a453bc1 && sbatch cartesius/prepare-lstm-wsd.job
git checkout ce8a024. Run at the same time: sbatch cartesius/exp-variation1.job and cartesius/sbatch exp-variation2.job
git checkout a74bda6. Preprocess to make binary files (the format is slightly different from the previous version): sbatch cartesius/prepare-lstm-wsd.job
git checkout e93fdb2. Run cartesius/exp-h256p64.sh (which calls sbatch)
When everything finishes, do git checkout 42bc700 and run sbatch cartesius/exp-variation-score.job

Reproduce (training speed) optimization experiment

Pre-process GigaWord into plain text (if you haven't done so): git checkout a74bda6 && sbatch process-gigaword.job
git checkout a74bda6
More preprocessing to make binary files: sbatch cartesius/prepare-lstm-wsd.job
git checkout e93fdb2
Run in parallel: sbatch cartesius/exp-optimization{i}.job where i=1,2,3,4

Data size experiment

Pre-process GigaWord into plain text (if you haven't done so): git checkout 694cb4d && sbatch process-gigaword.job
git checkout a74bda6
More preprocessing to make binary files: sbatch cartesius/prepare-lstm-wsd.job
git checkout 4e4a04a
Run sbatch cartesius/exp-data-size.job {i} with i="01",10,25,50,75

Model size experiements

Notice that there was uncertainty about the real version that produce h2048p512 and h512p128, see difference-edited.txt for a comparison with a recent version.

h=2048, p=512: git checkout 354acc1cfdd542142490afe40447cb6f40d2fd7c && ./train-lstm-wsd-full-data-google-model.job
h=512, p=128: git checkout 354acc1cfdd542142490afe40447cb6f40d2fd7c && ./train-lstm-wsd-full-data-large-model.job
h=512, p=64: see exp-h256p64.sh in "stability" section
h=100, p=10: see exp-variation*.job in "stability" section

Hyperparameter tuning for label propagation

git checkout 0448586
das5/exp-hyperp-label-propagation.sh (which calls sbatch)

Evaluating models

See evaluate/README.md.

Specific instructions for DAS-5

If you get error No module named 'pip' while importing spacy, you might want to log in to one of the compute nodes and install pip. For example:

ssh node057
easy_install-3.4 --user pip
python3 -c 'import pip'
exit

If you get No module named 'UserString' while loading a Word2vec model from disk, probably it is caused by the difference between compute node and login node. You might ssh to one of the compute nodes to continue your work.

Known issues

The reported results were produced using a model that didn't use <eos> (end of sentence) tokens, different from Yuan et al. We added <eos> in a later version.
The models were trained on sentences that were accidentally prepended with their length (e.g. "24 Under the settlements , including Georgia 's , Liggett agreed to put the warning ' ' smoking is addictive '' on its packs ."), this likely decreases the performance a bit.
On line 110 of the file evaluate/test-lstm_v2.py, should have been . We tried to rerun for one experiment with this change applied and found no difference in the results.

cltl / wsd-dynamic-sense-vector