| ID | 399 |
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| Original Title | Neural Machine Translation for Mooré, a Low-Resource Language |
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| Sanitized Title | neuralmachinetranslationformooréalowresourcelanguage |
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| Clean Title | Neural Machine Translation For Mooré, A Low-Resource Language |
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| Source ID | 2 |
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| Article Id01 | 625169600 |
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| Article Id02 | oai:HAL:hal-04425414v2 |
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| Corpus ID | (not set) |
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| Url | https://core.ac.uk/outputs/625169600 |
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| Original Abstract | Natural Language Processing (NLP) is a field of artificial intelligence with the goal of enabling machines to understand human language. Neural Machine Translation (NMT) is one of the many tools for NLP. It is used for the translation of a source language into a target language. NMT has made significant progress in recent years. However, most African languages, especially those in Burkina Faso, have received very little research attention in this context. In this article, we propose automated translation models forMooré language to French based on Transformers. We obtained an average BLUE score of 44.82 for the model trained on all the data and 65.75 for the model trained only with the Jehovah’s Witnesses Bible data for the machine translation task from Mooré to French. These encouraging results may evolve as the work is still in progress |
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| Pub Date | 2024-07-07 01:00:00 |
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| Pub Year | 2024 |
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| Search Term Used | Jehovah's AND yearPublished>=2024 |
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| Last Update | 2024-11-23 00:00:00 |
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| Status | 0 |
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| Modified | 2025-01-13 22:07:19 |
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| Created | 2025-01-13 22:07:19 |
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