Sequence-to-Sequence Learning as Beam-Search Optimization
Table 4: Machine translation experiments on test set; results below middle line are from MIXER model of Ranzato et al. (2016). SB-Δ indicates sentence BLEU costs are used in defining Δ. XENT is similar to our seq2seq model but with a convolutional encoder and simpler attention. DAD trains seq2seq with scheduled sampling (Bengio et al., 2015). BSO, SB-Δ experiments above have Ktr=6.
['[EMPTY]', 'Machine Translation (BLEU)  [ITALIC] Kte = 1', 'Machine Translation (BLEU)  [ITALIC] Kte = 5', 'Machine Translation (BLEU)  [ITALIC] Kte = 10']
For our main MT results, we set Δ(^y(k)1:t) to 1−SB(^y(K)r+1 This setting of Δ should act to penalize erroneous predictions with a relatively low sentence-level BLEU score more than those with a relatively high sentence-level BLEU score. While we start with an improved baseline, we see similarly large increases in accuracy as those obtained by DAD and MIXER, in particular when Kte>1.