Based on this line of code:
https://github.com/ufal/neuralmonkey/blob/master/neuralmonkey/decoders/output_projection.py#L125

Current implementation isn't flexible enough; if we train a "submodel" (e.g. decoder without attention - not containing any ctx_tensors) we cannot use the trained variables to initialize model with attention defined because the size of the dense layer matrix input become

https://github.com/JayParks/tf-seq2seq/blob/master/seq2seq_model.py#L368
It gives that the dimension 0 of inputs and attention do not match (as we are tile_batching it to batch_size * beam_width). Didn't you get any error while running with beam_search?

Thanks for this wonderful library, but it would be much more intuitive for users to get started by providing some simple but clearly training process on self-defined dataset.

This could be visualized in another table, where e.g. the confidence of the system across different documents could be compared and contrasted.