Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

Kód výsledku v 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>

Výsledek na webu

<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

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Jazyk výsledku

angličtina

Název v původním jazyce

Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

Popis výsledku anglicky

"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."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

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Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-195942993-7

ISSN

0736-587X

e-ISSN

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Počet stran výsledku

11

Strana od-do

27-37

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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