Papers by Xuanfu Wu
Generating Diverse Translation from Model Distribution with Dropout (2020.emnlp-main)
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| Challenge: | Existing neural machine translation models lack diversity in their generation. |
| Approach: | They propose to generate diverse translations by deriving Bayesian models and sampling models from them for inference. |
| Outcome: | The proposed method makes a better trade-off between diversity and accuracy. |
One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation (2022.naacl-main)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from multimodality problem . multi-modality is not solved by a teacher forcing algorithm, limiting model capability . |
| Approach: | They propose a method that generates multiple reference translations for each source sentence . they compare the NAT output with all references and select the one that best fits the simulated model . |
| Outcome: | The proposed method achieves 29.82 BLEU with only one decoding pass on WMT14 En-De . |
Mixup Decoding for Diverse Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing methods for generating multiple translations for source and target languages neglect the one-to-many mapping between the source and the target languages. |
| Approach: | They propose a method to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. |
| Outcome: | Experiments on WMT’16 en-ro, WMT'14 en de, and WMT ‘17 zh-en show that the proposed method outperforms all previous diverse machine translation methods. |