neural-machine-translation | neural machine translator using seq2seq | Translation library

I have a for loop with excerpts of try-except blocks referring to https://machinetalk.org/2019/03/29/neural-machine-translation-with-attention-mechanism/?unapproved=67&moderation-hash=ea8e5dcb97c8236f68291788fbd746a7#comment-67:-

I am building a toy encoder-decoder model for machine translation by using Tensorflow.

I use Tensorflow 1.8.0 cpu version. FastText pretrained word vector of 300 dimension is used in the embedding layer. Then the batch of training data goes through encoder and decoder with attention mechanism. In training stage decoder uses the TrainHelper and in inference stage GreedyEmbeddingHelper is used.

I already ran the model successfully by using a bidirectional LSTM encoder. However when I try to further improve my model by using multilayer LSTM, the bug arises. The code to build the training stage model is below:

For some self-studying, I'm trying to implement simple a sequence-to-sequence model using Keras. While I get the basic idea and there are several tutorials available online, I still struggle with some basic concepts when looking these tutorials:

• Keras Tutorial: I've tried to adopt this tutorial. Unfortunately, it is for character sequences, but I'm aiming for word sequences. There's is a block to explain the required for word sequences, but this is currently throwing "wrong dimension" errors -- but that's OK, probably some data preparation errors from my side. But more importantly, in this tutorial, I can clearly see the 2 types of input and 1 type of output: encoder_input_data, decoder_input_data, decoder_target_data
• MachineLearningMastery Tutorial: Here the network model looks very different, completely sequential with 1 input and 1 output. From what I can tell, here the decoder gets just the output of the encoder.

Is it correct to say that these are indeed two different approaches towards Seq2Seq? Which one is maybe better and why? Or do I read the 2nd tutorial wrongly? I already got an understanding in sequence classification and sequences labeling, but with sequence-to-sequence it hasn't properly clicked yet.