Papers by Antoine Nzeyimana
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
KinyaBERT: a Morphology-aware Kinyarwanda Language Model (2022.acl-long)
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| Challenge: | Pre-trained language models such as BERT are sub-optimal at handling morphologically rich languages. |
| Approach: | They propose a two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphology in a low-resource Kinyarwanda language. |
| Outcome: | The proposed model outperforms baseline models on the low-resource morphologically rich Kinyarwanda language by 2% in F1 score and 4.3% in average score of GLUE benchmark. |
Mitigating Tokenization-Induced Distance Distortion in Long-Context Multilingual Machine Translation (2026.acl-long)
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| Challenge: | Existing positional encodings rely on fixed token indices and implicitly assume uniform semantic density, which breaks down for long-context inputs. |
| Approach: | They propose a tokenization-aware adaptive positional encoding that conditions relative positional bias on input-level sequence length and fragmentation statistics. |
| Outcome: | The proposed model improves long-context robustness and accuracy over baselines. |
Morphological disambiguation from stemming data (2020.coling-main)
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| Challenge: | morphologically rich languages require ambiguous analysis to be effective . morphology tools are limited for morphlogical analysis, disambiguation, and annotation . |
| Approach: | They propose to learn to morphologically disambiguate Kinyarwanda verbal forms from a crowd-sourced stemming dataset using feature engineering and a feed-forward neural network based classifier. |
| Outcome: | The proposed method achieves about 89% non-contextualized disambiguation accuracy from a crowd-sourced dataset. |