Word-level Morpheme segmentation using Transformer neural network

Tsolmon Zundi, Chinbat Avaajargal


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.
Anthology ID:
2022.sigmorphon-1.15
Volume:
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
Month:
July
Year:
2022
Address:
Seattle, Washington
Venue:
SIGMORPHON
SIG:
SIGMORPHON
Publisher:
Association for Computational Linguistics
Note:
Pages:
139–143
Language:
URL:
https://aclanthology.org/2022.sigmorphon-1.15
DOI:
10.18653/v1/2022.sigmorphon-1.15
Bibkey:
Cite (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.
Cite (Informal):
Word-level Morpheme segmentation using Transformer neural network (Zundi & Avaajargal, SIGMORPHON 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.sigmorphon-1.15.pdf