From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation
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arXiv
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| Format: | Preprint |
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2024
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| author | Marashian, Ali Rice, Enora Gessler, Luke Palmer, Alexis von der Wense, Katharina |
| author_facet | Marashian, Ali Rice, Enora Gessler, Luke Palmer, Alexis von der Wense, Katharina |
| contents | Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only available parallel data are small amounts of religious texts. Hence, domain adaptation (DA) is a crucial issue faced by contemporary NMT and has, so far, been underexplored for low-resource languages. In this paper, we evaluate a set of methods from both low-resource NMT and DA in a realistic setting, in which we aim to translate between a high-resource and a low-resource language with access to only: a) parallel Bible data, b) a bilingual dictionary, and c) a monolingual target-domain corpus in the high-resource language. Our results show that the effectiveness of the tested methods varies, with the simplest one, DALI, being most effective. We follow up with a small human evaluation of DALI, which shows that there is still a need for more careful investigation of how to accomplish DA for low-resource NMT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00966 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation Marashian, Ali Rice, Enora Gessler, Luke Palmer, Alexis von der Wense, Katharina Computation and Language Machine Learning Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only available parallel data are small amounts of religious texts. Hence, domain adaptation (DA) is a crucial issue faced by contemporary NMT and has, so far, been underexplored for low-resource languages. In this paper, we evaluate a set of methods from both low-resource NMT and DA in a realistic setting, in which we aim to translate between a high-resource and a low-resource language with access to only: a) parallel Bible data, b) a bilingual dictionary, and c) a monolingual target-domain corpus in the high-resource language. Our results show that the effectiveness of the tested methods varies, with the simplest one, DALI, being most effective. We follow up with a small human evaluation of DALI, which shows that there is still a need for more careful investigation of how to accomplish DA for low-resource NMT. |
| title | From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2412.00966 |