| Challenge: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework. |
| Approach: | They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer. |
| Outcome: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework. |
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
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Domain Adaptation of Neural Machine Translation by Lexicon Induction (P19-1)
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A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
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Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing (2020.acl-main)
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| Challenge: | Existing studies have focused on learning domain knowledge from multiple domains, but task-specific parameters hinder mutual transfer of knowledge between new domains. |
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| Challenge: | Existing studies show that NMT models perform poorly in specific domains when in-domain parallel corpora are scarce or nonexistent. |
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Domain Adaptive Inference for Neural Machine Translation (P19-1)
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Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
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