LAVA NAT: A Non-Autoregressive Translation Model with Look-Around Decoding and Vocabulary Attention
Table 7: The effect of different decoding strategies.
['[BOLD] Model', '[BOLD] W’14 En→De', '[BOLD] I’14 De→En', '[BOLD] speedup']
We conduct comparative experiments on WMT14 En→De and IWSLT14 De→En datasets to analyze different decoding strategies. We also observe that L2R and R2L outperform greedy decoding, which proves that right and left tokens help mitigate the repeated generation problem.