Improving Fluency of Non-Autoregressive Machine Translation
Table 1: Quantitative results of the models in terms of BLEU score and average decoding times per sentence in milliseconds. Results on WMT14 English-German translation and results without back-translation are in the Appendix.
['Method', 'German WMT15 en → de', 'German WMT15 de → en', 'Romanian WMT16 en → ro', 'Romanian WMT16 ro → en', 'Czech WMT18 en → cs', 'Czech WMT18 cs → en', 'Decoding time [ms]']
We observe that the beam search greatly improves the translation quality over the CTC-based nAR models (“Non-autoregressive” vs. “Ours”). Additionally, we have control over the speed/quality trade-off by either lowering or increasing the beam size.