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        <title>Word-level Morpheme segmentation using Transformer neural network</title>
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        <namePart type="given">Tsolmon</namePart>
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            <namePart type="given">Garrett</namePart>
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    <abstract>This paper presents the submission of team NUM DI to the SIGMORPHON 2022 Task on Morpheme Segmentation Part 1, word-level morpheme segmentation. We explore the transformer neural network approach to the shared task. We develop monolingual models for world-level morpheme segmentation and focus on improving the model by using various training strategies to improve accuracy and generalization across languages.</abstract>
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    <identifier type="doi">10.18653/v1/2022.sigmorphon-1.15</identifier>
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%0 Conference Proceedings
%T Word-level Morpheme segmentation using Transformer neural network
%A Zundi, Tsolmon
%A Avaajargal, Chinbat
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%S Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F zundi-avaajargal-2022-word
%X This paper presents the submission of team NUM DI to the SIGMORPHON 2022 Task on Morpheme Segmentation Part 1, word-level morpheme segmentation. We explore the transformer neural network approach to the shared task. We develop monolingual models for world-level morpheme segmentation and focus on improving the model by using various training strategies to improve accuracy and generalization across languages.
%R 10.18653/v1/2022.sigmorphon-1.15
%U https://aclanthology.org/2022.sigmorphon-1.15
%U https://doi.org/10.18653/v1/2022.sigmorphon-1.15
%P 139-143

Markdown (Informal)

[Word-level Morpheme segmentation using Transformer neural network](https://aclanthology.org/2022.sigmorphon-1.15) (Zundi & Avaajargal, SIGMORPHON 2022)

• Word-level Morpheme segmentation using Transformer neural network (Zundi & Avaajargal, SIGMORPHON 2022)

ACL

• Tsolmon Zundi and Chinbat Avaajargal. 2022. Word-level Morpheme segmentation using Transformer neural network. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 139–143, Seattle, Washington. Association for Computational Linguistics.