Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages (2024.acl-long)
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| Challenge: | Multilingual neural machine translation systems learn to map sentences of different languages into a common representation space. |
| Approach: | They propose a setup where we decouple learning of vocabulary and syntax and train to translate while keeping those word representations frozen. |
| Outcome: | The proposed setup achieves near parity with a supervised setting on the TED domain with varying number of languages seen by the encoder. |
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| Challenge: | Recent work shows strong transfer learning capability to unseen languages in sequence-to-sequence neural networks . current transfer learning methods require much less downstream task data than would otherwise be required. |
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
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| Challenge: | Multilingual unsupervised machine translation is a computationally expensive and hard to tune approach . auxiliary parallel data is used to train translation systems from monolingual data . |
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