Soft Patterns

Text classification code using SoPa, based on "SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines" by Roy Schwartz, Sam Thomson and Noah A. Smith, ACL 2018

Setup

The code is implemented in python3.6 using pytorch. To run, we recommend using conda:

conda env create -f environment.yml
source activate sopa

Data format

The training and test code requires a two files for training, development and test: a data file and a labels file. Both files contain one line per sample. The data file contains the text, and the labels file contain the label. In addition, a word vector file is required (plain text, standard format of one line per vector, starting with the word, followed by the vector).

For other paramteres, run the following commands using the --help flag.

Training

To train our model, run

python3.6 ./soft_patterns.py \
    -e <word embeddings file> \
    --td <train data> \
    --tl <train labels> \
    --vd <dev data> \
    --vl <dev labels> \
    -p <pattern specification> \
    --model_save_dir <output model directory>

Test

To test our model, run

python3.6 ./soft_patterns_test.py \
    -e <word embeddings file> \
    --vd <test data> \
    --vl <test labels> \
    -p <pattern specification> \
    --input_model <input model>

Visualizing the Model

Under construction.

Sanity Tests

python -m unittest

References

If you make use if this code, please cite the following paper:

@inproceedings{Schwartz:2018,
  author={Schwartz, Roy and Thomson, Sam and Smith, Noah A.},
  title={{SoPa}: Bridging {CNNs}, {RNNs}, and Weighted Finite-State Machines},
  booktitle={Proc. of ACL},
  year={2018}
}

Contact

For questions, comments or feedback, please email roysch@cs.washington.edu

wbgxx333/soft_patterns