Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

Gulinigeer Abudouwaili, Kahaerjiang Abiderexiti, Nian Yi, Aishan Wumaier

Abstract
Due to the lack of data resources, rule-based or transfer learning is mainly used in the morphological tagging of low-resource languages. However, these methods require expert knowledge, ignore contextual features, and have error propagation. Therefore, we propose a joint morphological tagger for low-resource agglutinative languages to alleviate the above challenges. First, we represent the contextual input with multi-dimensional features of agglutinative words. Second, joint training reduces the direct impact of part-of-speech errors on morphological features and increases the indirect influence between the two types of labels through a fusion mechanism. Finally, our model separately predicts part-of-speech and morphological features. Part-of-speech tagging is regarded as sequence tagging. When predicting morphological features, two-label adjacency graphs are dynamically reconstructed by integrating multilingual global features and monolingual local features. Then, a graph convolution network is used to learn the higher-order intersection of labels. A series of experiments show that the proposed model in this paper is superior to other comparative models.
Anthology ID:
2023.sigmorphon-1.4
Volume:
Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Garrett Nicolai, Eleanor Chodroff, Frederic Mailhot, Çağrı Çöltekin
Venue:
SIGMORPHON
SIG:
SIGMORPHON
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–37
Language:
URL:
https://aclanthology.org/2023.sigmorphon-1.4
DOI:
10.18653/v1/2023.sigmorphon-1.4
Bibkey:
Cite (ACL):
Gulinigeer Abudouwaili, Kahaerjiang Abiderexiti, Nian Yi, and Aishan Wumaier. 2023. Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging. In Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 27–37, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging (Abudouwaili et al., SIGMORPHON 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.sigmorphon-1.4.pdf
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