Papers with NLLB-200
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations (2025.findings-acl)
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| Challenge: | addressing gender bias and maintaining logical coherence in machine translation remains challenging, especially when translating between natural gender languages, like English, and genderless languages, such as Persian, Indonesian, and Finnish. |
| Approach: | They propose a dataset to assess translation systems' performance in six low- to mid-resource languages and a translation dataset to examine gender bias and logical coherence. |
| Outcome: | The Translate-with-Care dataset, comprising 3,950 challenging scenarios across six low- to mid-resource languages, reveals a universal struggle in translating genderless content, resulting in gender stereotyping and reasoning errors. |
Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models (2026.eacl-long)
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| Challenge: | Existing evidence on the intrinsic difficulty of multilingual modeling is limited to small monolingual models or bilingual models trained from scratch. |
| Approach: | They propose to use typological properties to determine the difficulty of modeling a language . they analyze two large pre-trained multilingual translation models . |
| Outcome: | The proposed models are based on two large pre-trained models of encoder-decoder and decoder-only machine translation. |
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model (2023.acl-long)
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| Challenge: | Neural Machine Translation models are based on a Mixture of Experts architecture and can be pruned to remove up to 80% of experts without further finetuning. |
| Approach: | They propose a pruning method that removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
| Outcome: | The proposed pruning method removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
Bayelemabaga: Creating Resources for Bambara NLP (2025.naacl-long)
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| Challenge: | a lack of well-structured multilingual datasets remains a challenge for machine translation in under-resource languages. |
| Approach: | They propose to create a multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. |
| Outcome: | The proposed dataset is the most extensive curated multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. |
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
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| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |
Error Analysis of Multilingual Language Models in Machine Translation: A Case Study of English-Amharic Translation (2024.emnlp-main)
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| Challenge: | Multilingual large language models have significantly advanced machine translation, yet challenges remain for low-resource languages like Amharic. |
| Approach: | They evaluated the performance of NLLB-200 and M2M in English-Amharic bidirectional translation using the Lesan AI dataset. |
| Outcome: | The proposed models outperformed the existing models in English-Amharic bidirectional translation using the Lesan AI dataset. |
AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
| Challenge: | AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs . |
| Approach: | They propose a document-level multi-parallel translation dataset covering English and five African languages. |
| Outcome: | The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models . |