CopyNet Tensorflow Implementation

CopyNet Paper: Incorporating Copying Mechanism in Sequence-to-Sequence Learning.

CopyNet mechanism is wrapped with an exsiting RNN cell and used as an normal RNN cell.

Official nmt is also modified to enable CopyNet mechanism.

Usage

1. Use with tf.contrib.seq2seq

Just wrapper an any existing rnn cell(BasicLSTMCell, AttentionWrapper and so on).

cell = any_rnn_cell

copynet_cell = CopyNetWrapper(cell, encoder_outputs, encoder_input_ids,
    encoder_vocab_size,decoder_vocab_size)
decoder_initial_state = copynet_cell.zero_state(batch_size,
    tf.float32).clone(cell_state=decoder_initial_state)

helper = tf.contrib.seq2seq.TrainingHelper(...)
decoder = tf.contrib.seq2seq.BasicDecoder(copynet_cell, helper,
    decoder_initial_state, output_layer=None)
decoder_outputs, final_state, coder_seq_length = tf.contrib.seq2seq.dynamic_decode(decoder=decoder)
decoder_logits, decoder_ids = decoder_outputs

2. Use with tensorflow official nmt

Just add --copynet argument to nmt command line, full nmt usage is in nmt.

python nmt.nmt.nmt.py --copynet ...other_nmt_arguments