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Transformer-based Text Generation for Code-Switched Sepedi-English News

Domaine:

natural language processing

Type de record:

datasetpaper
Créateur:
Ramalepe, SimonModipa, ThipeDavel, Marelie
Éditeur:
Pillay, Anban W.Jembere, EdgarGerber, Aurona
Éditeur:
Steering Committee of the Southern African Conference for Artificial Intelligence Research, South Africa
Hôte:avatar

Code-switched data is rarely available in written form and this makes the development of large datasets required to train codeswitched language models difficult. Currently, available Sepedi-English code-switched corpora are not large enough to train a Transformer-based model for this language pair. In prior work, larger synthetic datasets have been constructed using a combination of a monolingual and a parallel corpus to approximate authentic code-switched text. In this study, we develop and analyse a new Sepedi-English news dataset (SepEnews). We collect and curate data from local radio news bulletins and use this to augment two existing sources collected from Sepedi newspapers and news headlines, respectively. We then develop and train a Transformer-based model for generating historic code-switched news, and demonstrate and analyse the system's performance.

Visit

doi.org

Tasks

code switchingnatural language generation

Languages

Sotho, Northern

Tags

Code-switched dataTransformer modelText generation.

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution Share Alike 4.0 Internationalhttps://creativecommons.org/licenses/by-sa/4.0/legalcode

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