Abstract
Mongolian morphological analysis (MMA) includes two subtasks: morphological segmentation and morphological tagging. It is a crucial preprocessing step in many Mongolian NLP applications. Recently, end-to-end neural approaches have achieved excellent results in the MMA task. However, these approaches handle morphological segmentation and morphological tagging independently, and ignore the relationship between the two subtasks. In this paper, we propose a multi-task sequence-to-sequence model for the MMA task that learns Mongolian morphological segmentation and tagging jointly. The proposed neural model introduces a shared morphological feature encoder to learn character-level and context-level word information. Besides, we design a flat joint attention decoder and a hierarchical joint attention decoder to generate Mongolian segmentation and tagging results, respectively. We employ the dynamic weight scheme to optimize and balance the weights between the two subtasks in MMA. We compare the proposed model with the baselines and evaluate the effectiveness of the sub-modules in the experiment. The result suggests that the proposed MMA model outperformed the state-of-the-art baselines.
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Acknowledgments
This work was funded by research program of science and technology at Universities of Inner Mongolia Autonomous Region (Grant No. NJZZ22251), 2022 Inner Mongolia Talent Support Project(DC2300001440), Inner Mongolia Natural Science Foundation (2022MS06013), Universities directly under the autonomous region Funded by the Fundamental Research Fund Project (JY20220122), Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (NJYT23059).
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Liu, N., Qing-Dao-Er-Ji, R., Su, X., Ji, Y., Aodengbala, Liu, G. (2023). Multi-task Learning for Mongolian Morphological Analysis. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_6
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