Papers by C. Downey
Targeted Multilingual Adaptation for Low-resource Language Families (2024.findings-emnlp)
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| Challenge: | Massively multilingual models are known to have limited utility in any one language, and to perform poorly on low-resource languages. |
| Approach: | They propose to adapt a pre-trained multilingual model to a language family and evaluate its performance on two downstream tasks and 11 evaluation languages. |
| Outcome: | The proposed model outperforms mono- and multilingual models on two downstream tasks and 11 evaluation languages. |
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages (2022.acl-long)
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| Challenge: | Unsupervised sequence segmentation is a key component of low-resource languages where there is little or no gold-standard data on which to train supervised models. |
| Approach: | They propose to pre-train a Masked Segmental Language Model multilingually to achieve unsupervised segmentation performance in extremely low-resource languages. |
| Outcome: | The proposed model outperforms a monolingual model and a pre-trained model on Quechua in 6/10 settings. |