Papers with machine translation quality
Quantifying Synthesis and Fusion and their Impact on Machine Translation (2022.naacl-main)
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| Challenge: | Literature in Natural Language Processing (NLP) typically labels whole language with strict type of morphology, e.g. fusional or agglutinative. |
| Approach: | They propose to quantify morphological typology at the word and segment level by using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). |
| Outcome: | The proposed method reduces the rigidity of NLP classification claims by measuring morphological diversity at the word and segment level. |
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. |