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Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A6I463P5K" target="_blank" >RIV/00216208:11320/23:6I463P5K - isvavai.cz</a>

  • Result on the web

    <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175400093&partnerID=40&md5=ef3ad3744f7fcd1c6c03fed1b40ec5ef" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175400093&partnerID=40&md5=ef3ad3744f7fcd1c6c03fed1b40ec5ef</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

  • Original language description

    "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. © 2023 Association for Computational Linguistics."

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    "Proc. Annu. Meet. Assoc. Comput Linguist."

  • ISBN

    978-195942993-7

  • ISSN

    0736-587X

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    27-37

  • Publisher name

    Association for Computational Linguistics (ACL)

  • Place of publication

  • Event location

    Cham

  • Event date

    Jan 1, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article