如：xxx app + www.xxx.com

不要在此issue里面提各种问题，谢谢配合。

I add a function(according the code in the tutorial4) to calculate the bleu score, but i get the vey low score(0.09), could you tell me why?
This is code to calculate bleu:

def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):
    
    model.eval()
        
    if isinstance(sentence, str):
        nlp = spacy.load('de')
        tokens = [token.te

    assert len(references) == len(hypotheses)

# Calculate BLEU-4 scores
bleu4 = corpus_bleu(references, hypotheses)

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?