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. The following code creates a new conda environment and activates it:

./install.sh
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>

Plots

To reproduce the numbers on the plots in our project writeup, run the corresponding .sh file in the plots folder.

For the tables, add the appropriate flags in train.sh and test.sh. The flags are: -b

--no_eps for deactivating epsilon transitions

--no_sl Don't use self loops

--shared_sl Share main path and self loop parameters, where self loops are discounted by a self_loop_parameter. 0 is default. 1 is one scalar parameter per state per pattern. 2 is one single global parameter

To change the number of diagonals, one has to change manually the self.num_diags parameter on lines 234 and 259. The defaults are 1 at line 234 and 2 at line 259. To increase the number of diagonals by k, one should change the values to 1+k and 2+k .

Sample data

The data/ folder contains sample files for training, development and testing. The data comes from the SST dataset (with a 100 training samples).

Each fold X (train, dev, test) contains two file: X.data (plain text sentences, one sentence per line) and X.labels (one label per line).

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 [email protected]