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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.19726 |
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| _version_ | 1866917852064251904 |
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| author | Billah, Syed Mohammed Mostaque Subarna, Ateya Ahmed Sarna, Sudipta Nandi Wasit, Ahmad Shawkat Fariha, Anika Sushmit, Asif Sadeque, Arig Yousuf |
| author_facet | Billah, Syed Mohammed Mostaque Subarna, Ateya Ahmed Sarna, Sudipta Nandi Wasit, Ahmad Shawkat Fariha, Anika Sushmit, Asif Sadeque, Arig Yousuf |
| contents | Around seven million individuals in India, Bangladesh, Bhutan, and Nepal speak Santali, positioning it as nearly the third most commonly used Austroasiatic language. Despite its prominence among the Austroasiatic language family's Munda subfamily, Santali lacks global recognition. Currently, no translation models exist for the Santali language. Our paper aims to include Santali to the NPL spectrum. We aim to examine the feasibility of building Santali translation models based on available Santali corpora. The paper successfully addressed the low-resource problem and, with promising results, examined the possibility of creating a functional Santali machine translation model in a low-resource setup. Our study shows that Santali-English parallel corpus performs better when in transformers like mt5 as opposed to untrained transformers, proving that transfer learning can be a viable technique that works with Santali language. Besides the mT5 transformer, Santali-English performs better than Santali-Bangla parallel corpus as the mT5 has been trained in way more English data than Bangla data. Lastly, our study shows that with data augmentation, our model performs better. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Santali Linguistic Inclusion: Building the First Santali-to-English Translation Model using mT5 Transformer and Data Augmentation Billah, Syed Mohammed Mostaque Subarna, Ateya Ahmed Sarna, Sudipta Nandi Wasit, Ahmad Shawkat Fariha, Anika Sushmit, Asif Sadeque, Arig Yousuf Computation and Language Machine Learning Around seven million individuals in India, Bangladesh, Bhutan, and Nepal speak Santali, positioning it as nearly the third most commonly used Austroasiatic language. Despite its prominence among the Austroasiatic language family's Munda subfamily, Santali lacks global recognition. Currently, no translation models exist for the Santali language. Our paper aims to include Santali to the NPL spectrum. We aim to examine the feasibility of building Santali translation models based on available Santali corpora. The paper successfully addressed the low-resource problem and, with promising results, examined the possibility of creating a functional Santali machine translation model in a low-resource setup. Our study shows that Santali-English parallel corpus performs better when in transformers like mt5 as opposed to untrained transformers, proving that transfer learning can be a viable technique that works with Santali language. Besides the mT5 transformer, Santali-English performs better than Santali-Bangla parallel corpus as the mT5 has been trained in way more English data than Bangla data. Lastly, our study shows that with data augmentation, our model performs better. |
| title | Towards Santali Linguistic Inclusion: Building the First Santali-to-English Translation Model using mT5 Transformer and Data Augmentation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2411.19726 |