Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation (2025.acl-long)
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| Challenge: | Multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages. |
| Approach: | They propose a method that inserts a set of tokens specifying the target language into the input sequence between the source and target tokens. |
| Outcome: | The proposed method outperforms existing models on a large-scale benchmark. |
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| Challenge: | Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions. |
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Multilingual Agreement for Multilingual Neural Machine Translation (2021.acl-short)
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Multimodal Neural Machine Translation: A Survey of the State of the Art (2025.emnlp-main)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
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| Challenge: | Existing multilingual neural machine translation models perform poorly on language pairs with no parallel corpus. |
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Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
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| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
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