Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model
Table 3: NPAD with beam search (NPAD+B). We report the NPAD+B’s with the best average log-probability on the validation set.
['Strategy', 'Beam Width', '[ITALIC] σ0', '# Chains', 'Valid NLL↓', 'Valid BLEU↑', 'Test-1 NLL↓', 'Test-1 BLEU↑']
S5SS3SSS0Px3 NPAD with Beam Search As described earlier, NPAD can be used with any other deterministic decoding strategy. Hence, we test it together with the beam search strategy. However, as the beam search is already able to find a good solution, the improvement is much smaller than that against the greedy strategy. For instance, comparing the greedy decoding and beam search with with 10, the differences without and with NPAD are 7.9617 vs. 0.7789 (NLL) and 1.66 vs. 0.43 (BLEU). This again confirms that the proposed NPAD has a potential to make the neural machine translation more suitable for deploying in the real world.