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Cross-Lingual Transfer Learning for Bambara Leveraging Resources From Other Languages

Domain:

natural language processing

Record type:

paper
Creator:
OusSatSha
Publisher:
IGI Global
Host:
Bambara, a language spoken primarily in West Africa, faces resource limitations that hinder the development of natural language processing (NLP) applications. This chapter presents a comprehensive cross-lingual transfer learning (CTL) approach to harness knowledge from other languages and substantially improve the performance of Bambara NLP tasks. The authors meticulously outline the methodology, including the creation of a Bambara corpus, training a CTL classifier, evaluating its performance across different languages, conducting a rigorous comparative analysis against baseline methods, and providing insights into future research directions. The results indicate that CTL is a promising and feasible approach to elevate the effectiveness of NLP tasks in Bambara.

Visit

doi.org

Tasks

transfer learning

Languages

BamanankanLame

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