Multilingual Non-Autoregressive Machine Translation without Knowledge Distillation
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929702143262720 |
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| author | Huang, Chenyang Huang, Fei Zheng, Zaixiang Zaïane, Osmar R. Zhou, Hao Mou, Lili |
| author_facet | Huang, Chenyang Huang, Fei Zheng, Zaixiang Zaïane, Osmar R. Zhou, Hao Mou, Lili |
| contents | Multilingual neural machine translation (MNMT) aims at using one single model for multiple translation directions. Recent work applies non-autoregressive Transformers to improve the efficiency of MNMT, but requires expensive knowledge distillation (KD) processes. To this end, we propose an M-DAT approach to non-autoregressive multilingual machine translation. Our system leverages the recent advance of the directed acyclic Transformer (DAT), which does not require KD. We further propose a pivot back-translation (PivotBT) approach to improve the generalization to unseen translation directions. Experiments show that our M-DAT achieves state-of-the-art performance in non-autoregressive MNMT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04537 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multilingual Non-Autoregressive Machine Translation without Knowledge Distillation Huang, Chenyang Huang, Fei Zheng, Zaixiang Zaïane, Osmar R. Zhou, Hao Mou, Lili Computation and Language Multilingual neural machine translation (MNMT) aims at using one single model for multiple translation directions. Recent work applies non-autoregressive Transformers to improve the efficiency of MNMT, but requires expensive knowledge distillation (KD) processes. To this end, we propose an M-DAT approach to non-autoregressive multilingual machine translation. Our system leverages the recent advance of the directed acyclic Transformer (DAT), which does not require KD. We further propose a pivot back-translation (PivotBT) approach to improve the generalization to unseen translation directions. Experiments show that our M-DAT achieves state-of-the-art performance in non-autoregressive MNMT. |
| title | Multilingual Non-Autoregressive Machine Translation without Knowledge Distillation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.04537 |