@inproceedings{abudouwaili-etal-2023-joint,
title = "Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging",
author = "Abudouwaili, Gulinigeer and
Abiderexiti, Kahaerjiang and
Yi, Nian and
Wumaier, Aishan",
editor = {Nicolai, Garrett and
Chodroff, Eleanor and
Mailhot, Frederic and
{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigmorphon-1.4",
doi = "10.18653/v1/2023.sigmorphon-1.4",
pages = "27--37",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging
%A Abudouwaili, Gulinigeer
%A Abiderexiti, Kahaerjiang
%A Yi, Nian
%A Wumaier, Aishan
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F abudouwaili-etal-2023-joint
%X 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.
%R 10.18653/v1/2023.sigmorphon-1.4
%U https://aclanthology.org/2023.sigmorphon-1.4
%U https://doi.org/10.18653/v1/2023.sigmorphon-1.4
%P 27-37
Markdown (Informal)
[Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging](https://aclanthology.org/2023.sigmorphon-1.4) (Abudouwaili et al., SIGMORPHON 2023)
ACL