Papers with lifelong learning settings
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)
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| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
| Approach: | They propose an encoder-decoder approach that can be extended to new languages by learning their corresponding modules. |
| Outcome: | The proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average while allowing to add new languages without retraining the rest of the modules. |