A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

Code and model files for the paper: "A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction" (In AAAI-18). If you use any part of this work, make sure you include the following citation:

@InProceedings{chollampatt2018mlconv,
  author    = {Chollampatt, Shamil and Ng, Hwee Tou},
  title     = {A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction},
  booktitle = {Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence},
  month     = {February},
  year      = {2018},
}

Setting Up

1. Clone this repository.
2. Download the pre-requisite software:
   • Fairseq-py
   • Subword-NMT
   • N-best Reranker

For training models, we suggest that you download the exact revisions of the above software. Go to software/ directory and run download.sh directory to download the exact revisions of these software. 3. Compile and install Fairseq-py.

For testing with pre-trained models

1. Go to data/ directory and run prepare_test_data.sh script to download and process CoNLL-2014 test dataset
2. Go to models/ directory and run download.sh to download the required model files
3. For running the system, run the run.sh script with the following format

./run.sh <input-file> <output-directory> <gpu-device-number> <models-path>

<input-file>: path to tokenized input data <gpu-device-number>: typically 0,1,2 etc to be used with the environment variable CUDA_VISIBLE_DEVICES <models-path>: could be the path to a single model file or a directory having multiple model files alone.

You can also run the script by adding optional arguments for re-ranking

./run.sh <input-file> <output-directory> <gpu-device-number> <models-path> <weights-file> <features>

<wegihts-file>: path to trained feature weights for the re-ranker (within models/reranker_weights <features>: use 'eo' for edit operation features, and 'eolm' for both edit operations and language model features.

For training from scratch

In the training/ directory, within the preprocess.sh script, place paths to the the training datasets and development datasets. The source and target files must be tokenized.

1. Go to training/ directory
2. Run ./preprocess.sh script
3. To train the models by initializing with pre-trained word embeddings, run train_embed.sh. To train the models without pre-trainined embeddings use the train.sh script instead.
4. To train the re-ranker, you would additionally need to have compiled Moses software. Run train_reranker.sh script with the following arguments:

./train_reranker.sh <output_dir> <gpu-device-number> <models-path> <path-to-moses>

<output-dir>: directory to store temporary files and final output weights.txt file.

License

The code and models in this repository are licensed under the GNU General Public License Version 3. For commercial use of this code and models, separate commercial licensing is also available. Please contact:

• Shamil Chollampatt (shamil@u.nus.edu)
• Hwee Tou Ng (nght@comp.nus.edu.sg)