Papers by Udia Markus
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (2020.emnlp-main)
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| Challenge: | Recent studies show that results from high-resource languages cannot be easily transferred to realistic, low-resourced scenarios. |
| Approach: | They analyse performance of multilingual transformer models using available resources for Hausa, isiXhosa and NER and topic classification. |
| Outcome: | The proposed models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. |