Keras sequence-to-sequence encoder-decoder part-of-speech tagging example with attention mechanism
<p>I have a sequence of indexed words w_1, ..., w_n. Since I'm new to deep learning, I am looking for a simple implementation of a seq2seq pos tagging model in Keras which uses attention mechanism and produces a sequence of POS tags t_1, ..., t_n out of my word sequence.</p>

<p>To be specific, I don't know how to gather the outputs of LSTM hidden layers of the encoder (since they are TimeDistributed) and how to feed the decoder LSTM layer for each timestamp with the outputs of time "t-1" for generating the output "t".</p>

<p>The model I'm thinking about is looking like the one in this paper <a href="http://arxiv.org/abs/1409.0473" rel="nofollow">http://arxiv.org/abs/1409.0473</a>.</p>