Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging
Gulinigeer Abudouwaili, Kahaerjiang Abiderexiti, Nian Yi, Aishan Wumaier · 2023
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 higherorder intersection of labels.A series of experiments show that the proposed model in this paper is superior to other comparative models.