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        <title>GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages</title>
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        <namePart type="given">Fitsum</namePart>
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    <abstract>Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications. Low-resourced languages with similar typologies are generally confused with each other in real-world applications such as machine translation, affecting the user’s experience. In this work, we present a language identification dataset for five typologically and phylogenetically related low-resourced East African languages that use the Ge’ez script as a writing system; namely Amharic, Blin, Ge’ez, Tigre, and Tigrinya. The dataset is built automatically from selected data sources, but we also performed a manual evaluation to assess its quality. Our approach to constructing the dataset is cost-effective and applicable to other low-resource languages. We integrated the dataset into an existing language-identification tool and also fine-tuned several Transformer based language models, achieving very strong results in all cases. While the task of language identification is easy for the informed person, such datasets can make a difference in real-world deployments and also serve as part of a benchmark for language understanding in the target languages. The data and models are made available at https://github.com/fgaim/geezswitch.</abstract>
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%0 Conference Proceedings
%T GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages
%A Gaim, Fitsum
%A Yang, Wonsuk
%A Park, Jong C.
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F gaim-etal-2022-geezswitch
%X Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications. Low-resourced languages with similar typologies are generally confused with each other in real-world applications such as machine translation, affecting the user’s experience. In this work, we present a language identification dataset for five typologically and phylogenetically related low-resourced East African languages that use the Ge’ez script as a writing system; namely Amharic, Blin, Ge’ez, Tigre, and Tigrinya. The dataset is built automatically from selected data sources, but we also performed a manual evaluation to assess its quality. Our approach to constructing the dataset is cost-effective and applicable to other low-resource languages. We integrated the dataset into an existing language-identification tool and also fine-tuned several Transformer based language models, achieving very strong results in all cases. While the task of language identification is easy for the informed person, such datasets can make a difference in real-world deployments and also serve as part of a benchmark for language understanding in the target languages. The data and models are made available at https://github.com/fgaim/geezswitch.
%U https://aclanthology.org/2022.lrec-1.707
%P 6578-6584

Markdown (Informal)

[GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages](https://aclanthology.org/2022.lrec-1.707) (Gaim et al., LREC 2022)

• GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages (Gaim et al., LREC 2022)

ACL

• Fitsum Gaim, Wonsuk Yang, and Jong C. Park. 2022. GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6578–6584, Marseille, France. European Language Resources Association.