Code for "VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling", to be presented at EMNLP 2020

git clone https://github.com/machelreid/vcdm.git
cd vcdm
wget https://machelreid.github.io/resources/Reid2020VCDM.zip #contains the oxford, urban (slang), and wiki (wikipedia) datasets
unzip Reid2020VCDM.zip
mv Reid2020VCDM/ data/
chmod +x sentence-bleu # for evaluation using `sent-bleu`

Note the following:

• All data IS pretokenized
• The input "example" field is preprocessed into the phrase-context pair format to be fed into the encoder.

python train.py --set data=DATASET_NAME arg1=ARG1 arg2=ARG2# etc...... check out `config/config.yaml` for all arguments

Default arguments can be seen and modified in config/config.yaml

If you find our code, or our work useful - please cite as:

@inproceedings{reid2020vcdm,
  title     = {VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling},
  author    = {Reid, Machel and Marrese-Taylor, Edison and Matsuo, Yutaka},
  year      = {2020},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  publisher = {Association for Computational Linguistics},
  code      = {https://github.com/machelreid/vcdm},
  preprint  = {https://arxiv.org/abs/2010.03124}
}

If you want to contact us about anything related to the work, feel free to reach out to me at machelreid -at- weblab -dot- t -dot- u-tokyo -dot- ac -dot- jp

(21/10/2020)

• Add easier evaluation functionality (add an --evaluate argument or something similar)
• Have a better README (hopefully!!)