A fully automated and scalable Parallel Data Augmentation for Low Resource Languages using Image and Text Analytics
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915555683860480 |
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| author | Sharma, Prawaal Goyal, Navneet Goyal, Poonam R, Vishnupriyan |
| author_facet | Sharma, Prawaal Goyal, Navneet Goyal, Poonam R, Vishnupriyan |
| contents | Linguistic diversity across the world creates a disparity with the availability of good quality digital language resources thereby restricting the technological benefits to majority of human population. The lack or absence of data resources makes it difficult to perform NLP tasks for low-resource languages. This paper presents a novel scalable and fully automated methodology to extract bilingual parallel corpora from newspaper articles using image and text analytics. We validate our approach by building parallel data corpus for two different language combinations and demonstrate the value of this dataset through a downstream task of machine translation and improve over the current baseline by close to 3 BLEU points. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A fully automated and scalable Parallel Data Augmentation for Low Resource Languages using Image and Text Analytics Sharma, Prawaal Goyal, Navneet Goyal, Poonam R, Vishnupriyan Computation and Language Linguistic diversity across the world creates a disparity with the availability of good quality digital language resources thereby restricting the technological benefits to majority of human population. The lack or absence of data resources makes it difficult to perform NLP tasks for low-resource languages. This paper presents a novel scalable and fully automated methodology to extract bilingual parallel corpora from newspaper articles using image and text analytics. We validate our approach by building parallel data corpus for two different language combinations and demonstrate the value of this dataset through a downstream task of machine translation and improve over the current baseline by close to 3 BLEU points. |
| title | A fully automated and scalable Parallel Data Augmentation for Low Resource Languages using Image and Text Analytics |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.13211 |