Papers with WMT16 Ro→En
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate (2021.emnlp-main)
Copied to clipboard
| Challenge: | Non-autoregressive neural machine translation models suffer from the multi-modality problem . aligNART leverages full alignment information to explicitly reduce the modality of the target distribution . |
| Approach: | They propose an alignment decomposition method which explicitly reduces the modality of the target distribution. |
| Outcome: | The proposed model outperforms previous models that focus on modality reduction on two translation tasks. |
Imitation Learning for Non-Autoregressive Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Existing non-autoregressive translation models lack parallel decoding, which is a bottleneck for NMT decoding. |
| Approach: | They propose a framework for non-autoregressive machine translation that emulates the autoregressive model by sampling sentence length in parallel. |
| Outcome: | The proposed model achieves 31.85 BLEU on WMT16 RoEn and 30.68 BLUE on IWSLT16 EnDe on the IWSLD16, WMT14 and WMT15 datasets. |