Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity (2024.findings-eacl)
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Eric Khiu, Hasti Toossi, Jinyu Liu, Jiaxu Li, David Anugraha, Juan Flores, Leandro Roman, A. Seza Doğruöz, En-Shiun Lee
| Challenge: | Existing approaches for predicting the performance of NLP models for low-resource languages (LRLs) focus on high-resourced languages, overlooking LRLs and domain shifts. |
| Approach: | They investigate the impact of domain similarity on predicting performance of machine translation models in low-resource languages. |
| Outcome: | The results show that domain similarity has the most important impact on predicting the performance of Machine Translation models. |
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