Papers by Jesujoba O. Alabi

2 papers
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (2022.coling-1)

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Challenge: Multilingual pre-trained language models have shown impressive performance on several downstream tasks for both high-resourced and low-resource languages.
Approach: They propose to apply multilingual adaptive fine-tuning to 17 most-resourced African languages and three other high-resource languages to encourage cross-lingual transfer learning.
Outcome: The proposed approach is competitive to LAFT on individual languages while requiring significantly less disk space.
Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning (2022.coling-1)

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Challenge: Currently, low resource languages are not supported by proper translation systems or parallel corpus.
Approach: They propose to fine-tune the pretrained language models to generate utterances in English-to-Pidgin by leveraging the proximity of the source and target languages and using positive and negative examples in constrastive training objectives.
Outcome: The proposed method is sufficient to generate utterances in English-to-Pidgin, which are two closely-related languages.

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