Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation (2020.tacl-1)
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| Challenge: | Neural machine translation systems are usually trained on clean parallel data, but the quality of translations is poor when translating noisy texts. |
| Approach: | They synthesize parallel data of UGT and exploit monolingual data to generate translations . they propose to use monolingual parallel data to train or adapt NMT systems . |
| Outcome: | The proposed approach improves the translation quality of noisy texts while making them more robust. |
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