ICL: Iterative Continual Learning for Multi-domain Neural Machine Translation (2024.findings-emnlp)
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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. |
| Approach: | They propose an iterative Continual learning framework for multi-domain neural machine translation that leverages previously acquired domain knowledge. |
| Outcome: | The proposed model outperforms baseline models on UM-Corpus and OPUS datasets. |
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