This word-level loss ensures efficient and scalable training of seq2seq models.
this word-level training objective suffers from a few crucial limitations, namely the label bias, the exposure bias, and the loss-evaluation mismatch (Lafferty et al., 2001; Figure 1: The BLEU score of BSO decreases after beam size 3 as results of increasing length ratio 1 in German→English translation.