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Convolutional Embedding for Edit Distance (SIGIR 20)

In this project, we design and implement a deep learning model, which transforms strings into real number vectors while preserving their neighboring relation. Specifically, if the edit distance of two strings x and y is small, the L2-distance of their embeddings should also be small. With this model, we can transform expensive edit distance computation to cheaper L2-distance computation and speed up string similarity search.

before run

Please install PyTorch refer to PyTorch and install Levenshtein and transformers via

pip install python-Levenshtein
pip install transformers

start training

• train CNN-ED model

python main.py --dataset word --nt 1000 --nq 1000 --epochs 20 --save-split --recall

• test bert embedding

python main.py --dataset word --nt 1000 --nq 1000 --bert --save-split --recall

optional arguments:

  -h, --help            show this help message and exit
  --dataset             dataset name which is under folder ./data/
  --nt                  # of training samples
  --nr                  # of generated training samples
  --nq                  # of query items
  --nb                  # of base items
  --k                   # sampling threshold
  --epochs              # of epochs
  --shuffle-seed        seed for shuffle
  --batch-size          batch size for sgd
  --test-batch-size     batch size for test
  --channel CHANNEL     # of channels
  --embed-dim           output dimension
  --save-model          save cnn model
  --save-split          save split data folder
  --save-embed          save embedding
  --random-train        generate random training samples and replace
  --random-append-train generate random training samples and append
  --embed-dir           embedding save location
  --recall              print recall
  --embed EMBED         embedding method
  --maxl MAXL           max length of strings
  --no-cuda             disables GPU training

reference

If you use this code, please cite the following paper

@inproceedings{cnned,
  author    = {Xinyan Dai and
               Xiao Yan and
               Kaiwen Zhou and
               Yuxuan Wang and
               Han Yang and
               James Cheng},
  title     = {Convolutional Embedding for Edit Distance},
  booktitle = {Proceedings of the 43rd International {ACM} {SIGIR} conference on
               research and development in Information Retrieval, {SIGIR} 2020, Virtual
               Event, China, July 25-30, 2020},
  pages     = {599--608},
  publisher = {{ACM}},
  year      = {2020},
  url       = {https://doi.org/10.1145/3397271.3401045},
  doi       = {10.1145/3397271.3401045},
}