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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.19522 |
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| _version_ | 1866917361874894848 |
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| author | Ranathungaa, Surangika Nayak, Shravan Huang, Shih-Ting Cindy Mao, Yanke Su, Tong Chan, Yun-Hsiang Ray Yuan, Songchen Rinaldi, Anthony Lee, Annie En-Shiun |
| author_facet | Ranathungaa, Surangika Nayak, Shravan Huang, Shih-Ting Cindy Mao, Yanke Su, Tong Chan, Yun-Hsiang Ray Yuan, Songchen Rinaldi, Anthony Lee, Annie En-Shiun |
| contents | Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation Ranathungaa, Surangika Nayak, Shravan Huang, Shih-Ting Cindy Mao, Yanke Su, Tong Chan, Yun-Hsiang Ray Yuan, Songchen Rinaldi, Anthony Lee, Annie En-Shiun Computation and Language Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs. |
| title | Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.19522 |