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.

Since in copynet scenarios the target sequence contains words from source sentences, the best choice is to use a shared vocabulary for source vocabulary and target vcabulary. And we also use a parameter generated vocabulary size, namely, the number of target vocabulary excluding words from source sequences (copied words), to indicate that the first N(=generated vocabulary size) words in shared vocabulary are in generate mode and target word indexes larger than N are copied.

In this codebase, vocab_size and gen_vocab_size are variables representing shared vocabulary size and generated vocabulalry size.

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,
    vocab_size, gen_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

Full nmt usages are in nmt.

--copynet argument added to nmt command line to enable copy mechanism.

--share_vocab argument must be set.

--gen_vocab_size argument represents the size of generated vocabulary (excluding copy words from target vocabulary), if is not set, it equals the size of whole vocabulary.

python nmt.nmt.nmt.py --copynet --share_vocab --gen_vocab_size=2345 ...other_nmt_arguments