Learning When to Attend for Neural Machine Translation
Table 1: Main results on the datasets of NIST 2003, 2004, 2005, with NIST 2006 as the development set. ∗The results were achieved on a union set of NIST 2003, 2004, and 2005 data. † and ‡: significant over RNNSearch at the level of 0.05 and 0.01, respectively, as tested by bootstrap resampling Koehn (2014).
['#', 'System', '#Params', 'Time', 'NIST06', 'NIST03', 'NIST04', 'NIST05', 'All∗']
From the results we can see that the best adaptive attention model achieved an improvement of 0.7 BLEU score over RNNSearch on the development set. We then evaluated the same model on the test sets, and a significant improvement of 0.8 BLEU score was achieved over RNNSearch (the improvement over cdec is 2.0 BLEU score). On the other hand, we find that adaptive attention model incurs more parameters than RNNSearch (60.6M vs. 70.6M). And more training time is required (153minutes/epoch vs. 207minutes/epoch).