Papers with NART
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate (2021.emnlp-main)
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| 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. |
Hint-Based Training for Non-Autoregressive Machine Translation (D19-1)
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| Challenge: | AutoRegressive Translation models have to generate tokens sequentially during decoding and thus suffer from high inference latency. |
| Approach: | They propose to use hidden states and word alignments to help train NART models. |
| Outcome: | The proposed model improves on the WMT14 En-De and De-En datasets but is faster in inference than the current models. |