Papers by Minhan Xu
Sub-Word Alignment is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation (2022.acl-short)
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| Challenge: | Low-resource machine translation (MT) is challenging due to the scarcity of parallel data and lack of bilingual dictionaries. |
| Approach: | They propose to leverage embedding duplication between aligned sub-words to extend the Parent-Child transfer learning method to improve low-resource machine translation. |
| Outcome: | The proposed method achieves BLEU scores of 22.5, 28.0 and 18.1 respectively. |
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to train NMT models rely on sparse parallel data . a variety of PC variants yield significant improvements for low-resource NMT . |
| Approach: | They propose to transfer well-trained NMT models to low-resource languages by bidirectionally-adaptive learning strategy . they divide inner constituents of Parent encoder into two "teams" aiming to adapt to characteristics of low- and high-resourced languages . |
| Outcome: | The proposed method improves on low-resource NMT models with a variety of PC variants. |