Detecting Various Types of Noise for Neural Machine Translation (2022.findings-acl)
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| Challenge: | a recent study investigated the impact of noise on the performance of machine translation systems. |
| Approach: | They propose to combine recent research on data filtering with original analysis . they find that most of the suggested noise types can be detected with 90% accuracy . |
| Outcome: | The proposed filtering systems can detect noise types with 90% accuracy in high resource settings. |
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