FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Table 1: BLEU scores on three MT benchmark datasets for FlowSeq with argmax decoding and baselines with purely non-autoregressive decoding methods. The first and second block are results of models trained w/w.o. knowledge distillation, respectively.
['[BOLD] Models', '[BOLD] WMT2014  [BOLD] EN-DE', '[BOLD] WMT2014  [BOLD] DE-EN', '[BOLD] WMT2016  [BOLD] EN-RO', '[BOLD] WMT2016  [BOLD] RO-EN', '[BOLD] IWSLT2014  [BOLD] DE-EN']
The first block lists results of models trained on raw data, while the second block shows results using knowledge distillation. Without using knowledge distillation, the FlowSeq base model achieves significant improvements (more than 9 BLEU points) over the baselines. This demonstrates the effectiveness of FlowSeq in modeling complex interdependences in the target languages.