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Learning Stress in Arabic Low-Resource Settings

Domain:

natural language processing

Record type:

paper
Creator:
AbdOweJorJef
Publisher:
Uni
Host:avatar
We predict lexical stress in Arabic varieties from syllable structure, modeling stress assignment as generation: given an unstressed input, the system outputs a stress-marked word. We compare four approaches: a grammar induction algorithm (\bufia), a transformer-based neural network, a rule-based method derived from linguistic literature, and a frequency baseline. The models are evaluated across several low-resource settings by varying the training data size by words, structural type, and syllable count. {\bufia} outperforms the neural network, especially when data are scarce. This points to grammar induction as an interpretable and sample-efficient approach for learning stress. Society for Computation in Linguistics, 9(1)

Visit

doi.orgopenpublishing.library.umass.edu

Tags

Arabicstresslow-resourcelearninggrammar inductionBUFIAneural transductiondialectssyllablephonology+1

Licenses

Creative Commons Attribution 4.0https://creativecommons.org/licenses/by/4.0

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