Filtering Back-Translated Data in Unsupervised Neural Machine Translation (2020.coling-main)
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| Challenge: | Current state of the art approaches for unsupervised neural machine translation (NMT) use only monolingual data for training. |
| Approach: | They propose an approach to filter back-translated data as part of the training process of unsupervised neural machine translation (NMT) they propose a weight component based on the quality of pseudo parallel sentence pairs generated in back-translation phase. |
| Outcome: | The proposed approach improves the training performance of unsupervised neural machine translation systems by giving weight to good pseudo parallel sentence pairs in the back-translation phase. |
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