https://github.com/christof93/morphological-reinflection

Source code for the paper "Morphological Inflection Generation with Hard Monotonic Attention"

https://github.com/christof93/morphological-reinflection

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Source code for the paper "Morphological Inflection Generation with Hard Monotonic Attention"

https://github.com/Christof93/morphological-reinflection/blob/master/

# morphological-reinflection

Source code for the paper: [Sequence to Sequence Transduction with Hard Monotonic Attention](https://arxiv.org/abs/1611.01487).


Requires [dynet](https://github.com/clab/dynet).

Usage:

    hard_attention.py [--dynet-mem MEM][--input=INPUT] [--hidden=HIDDEN] [--feat-input=FEAT] [--epochs=EPOCHS] [--layers=LAYERS] [--optimization=OPTIMIZATION] [--reg=REGULARIZATION][--learning=LEARNING] [--plot] [--eval] [--ensemble=ENSEMBLE] TRAIN_PATH DEV_PATH TEST_PATH RESULTS_PATH SIGMORPHON_PATH...

Arguments:
* TRAIN_PATH    train set path
* DEV_PATH      development set path
* TEST_PATH     test set path
* RESULTS_PATH  results file (to be written)
* SIGMORPHON_PATH   sigmorphon root containing data, src dirs

Options:
* -h --help                     show this help message and exit
* --dynet-mem MEM               allocates MEM bytes for (py)cnn
* --input=INPUT                 input embeddings dimension
* --hidden=HIDDEN               lstm hidden layer dimension
* --feat-input=FEAT             feature embeddings dimension
* --epochs=EPOCHS               amount of training epochs
* --layers=LAYERS               amount of layers in lstm
* --optimization=OPTIMIZATION   chosen optimization method ADAM/SGD/ADAGRAD/MOMENTUM/ADADELTA
* --reg=REGULARIZATION          regularization parameter for optimization
* --learning=LEARNING           learning rate parameter for optimization
* --plot                        draw a learning curve plot while training each model
* --eval                        run evaluation on existing model (without training)
* --ensemble=ENSEMBLE           ensemble model paths, separated by comma

For example:

    python hard_attention.py --cnn-mem 4096 --input=100 --hidden=100 --feat-input=20 --epochs=100 --layers=2 --optimization=ADADELTA  /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-train /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-dev /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-test /Users/roeeaharoni/Dropbox/phd/research/morphology/inflection_generation/results/navajo_results.txt /Users/roeeaharoni/research_data/sigmorphon2016-master/
    
If you use this code for research purposes, please use the following citation:

    @article{aharoni2016sequence,
        title={Sequence to Sequence Transduction with Hard Monotonic Attention},
        author={Aharoni, Roee and Goldberg, Yoav},
        journal={arXiv preprint arXiv:1611.01487},
        year={2016}
    }

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