function `torch_bleu` producing inappropriate results

[jinulee-v]

Hi,

While playing around, I have found an error in the function torch_bleu which is used to rank batch-negatives and beam-positives.

In model.model.CoNTGenerator.torch_bleu (line 47-70), there is an severe mistake which results in wrong BLEU scores, certainly when n_gram == 1 and possibly when n_gram >= 2 (rare case where token indices are propotional; i.e. 2-gram [4, 8] and [34, 68]).

Current line 66-67:

sim_matrix = torch.cosine_similarity(input_tensor2_4gram.unsqueeze(3), input_tensor1_4gram.unsqueeze(2),
                                             dim=-1) >= 1.0

Suggestion:

sim_matrix = torch.norm( # Calculate L2 norm to find if N-gram in `sys`` is present in `ref``
        input_tensor2_4gram.unsqueeze(3) - input_tensor1_4gram.unsqueeze(2),
        p=2,
        dim=-1
) == 0.0