Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation
Table 4: The BLEU scores of our proposed TCL-NAT and the baseline methods on the IWSLT14 De-En, IWSLT16 En-De, WMT14 De-En and WMT14 En-De tasks. NPD 9 indicates results of noisy parallel decoding with 9 candidates, i.e., B = 4, otherwise B = 0.
['[BOLD] Models', 'IWSLT14 De-En', 'IWSLT16 En-De', 'WMT14 De-En', 'WMT14 En-De', 'Latency', 'Speedup']
We can see that TCL-NAT achieves significant improvements over all NAT baselines on different datasets. Specifically, we outperform ENAT and NAT-Reg with a notable margin. In addition, compared with NAT-Reg, we do not introduce any auxiliary loss functions in training stage and compared with ENAT, we just copy the source sentence as the decoder input, which does not add the extra workload in inference stage. Compared with FlowSeq, our method (without NPD) achieves better scores on most datasets with a much larger speedup. We also outperform FCL-NAT on most datasets with a less training steps. As for the inference efficiency, we achieve a 16.0 times speedup (NPD 9), which is comparable with state of the art methods (FCL-NAT and ENAT).