Chainer implementation of Pointer Sentinel Mixture Models(https://arxiv.org/abs/1609.07843)

Chainer v1.15.0.1

You can prepare traing data as train_data.txt. In train_data.txt, every line has one JSON which has input data and output data represented as list.
This means following format

{"input": ["I", "have", "a", "pen", ".", "I", "have", "an", "apple", "."], "output": ["apple", "pen"]}
{"input": ["I", "have", "a", "pen", ".", "I", "have", "a", "pineapple", "."], "output": ["pineapple", "pen"]}
...

And you must put train_data.txt on current directory.

PointerSentinelMixtureModels.py contains code for building model.
You can start training like following command:

$ python PointerSentinelMixtureModels.py --batchsize=64 --gpu=0 --embed=128 --unit=256 --out=result -L=30 --mode=train

If you want to restart traing by saves model, switch --mode parameter as --mode=restart,and add parameters for model and vocab, id2wd files, --model --vocab_path --id_path.
You can restart training like following command:

$ python  PointerSentinelMixtureModels.py --batchsize=64 --gpu=0 --embed=128 --unit=256 --out=result -L=30 --mode=restart --vocab_path=data/vocab --id_path=data/id2wd --model=result/PointerSentinelMixtureModels-2000.model

Meaing of parameters are written in PointerSentinelMixtureModels.py file. Please read once.

After training, you can start decoding. In this code, I used beam search algorithm(this is not refered in paper).
You can start decoding like following command:

$ python  PointerSentinelMixtureModels.py --embed=128 --unit=256  -L=30 --mode=decode --vocab_path=data/vocab --id_path=data/id2wd --model=result/PointerSentinelMixtureModels-500.model --data_path=train_data.txt --decode_path=decode.txt --beam_size=8 --max_length=30

--data_path is a parameter to specify file for encoding.
Encoding file is required JSON format which has input parameter that have array object in every line.

In code, information about training(epoch, barchsize, batch loss) is recorded in log file train.log.

INFO:root:Learning Start!
INFO:root:epoch: 1, batchsize: 64, loss: 4412.14013671875
INFO:root:epoch 1's calc time 32.55013418197632
INFO:root:epoch: 2, batchsize: 64, loss: 4932.1435546875
INFO:root:epoch 2's calc time 40.78377103805542
...

Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher, "Pointer Sentinel Mixture Models" arXiv:1609.07843