Neural networks with a large number of parameters have a serious problem with an overfitting. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. But during the test time, the dropout is turned off. More information in https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
If you want to enable dropout on an encoder or on the decoder, you can simply add dropout_keep_prob to the particular section:
[encoder] class=encoders.recurrent.SentenceEncoder dropout_keep_prob=0.8 ...
[decoder] class=decoders.decoder.Decoder dropout_keep_prob=0.8 ...
Detailed information in https://arxiv.org/abs/1512.05287
If you want allow dropout on the recurrent layer of your encoder, you can add use_pervasive_dropout parameter into it and then the dropout probability will be used:
[encoder] class=encoders.recurrent.SentenceEncoder dropout_keep_prob=0.8 use_pervasive_dropout=True ...
Attention Seeded by GIZA++ Word Alignments¶
todo: OC to reference the paper and describe how to use this in NM