Data Augmentation for Low-Resource Languages in Multilingual Dependency Parsing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AZALZ4Y9G" target="_blank" >RIV/00216208:11320/26:ZALZ4Y9G - isvavai.cz</a>

Výsledek na webu

<a href="https://www.jstage.jst.go.jp/article/jnlp/32/1/32_219/_article/-char/ja/" target="_blank" >https://www.jstage.jst.go.jp/article/jnlp/32/1/32_219/_article/-char/ja/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5715/jnlp.32.219" target="_blank" >10.5715/jnlp.32.219</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data Augmentation for Low-Resource Languages in Multilingual Dependency Parsing

Popis výsledku v původním jazyce

UDify (Kondratyuk and Straka 2019) is a multilingual, multi-task parser fine-tuned on mBERT that achieves remarkable performance on high-resource languages. However, on some low-resource languages, its performance saturates early and decreases gradually as training proceeds. To address this issue, this study applies a data augmentation method to improve parsing performance. We conducted experiments on five few-shot and three zero-shot languages to test the effectiveness of this approach. The unlabeled attachment scores were improved on the zero-shot language dependency parsing tasks, with the average score increasing from 55.6% to 59.0%. Meanwhile, dependency parsing tasks in high-resource languages and other Universal Dependencies tasks were almost unaffected. The experimental results demonstrate that the data augmentation method is effective for low-resource languages in multilingual dependency parsing. Furthermore, our experiments confirm that continuously increasing the quantity of synthetic data enhances UDify's performance. This improvement was particularly effective for zero-shot target languages.

Název v anglickém jazyce

Data Augmentation for Low-Resource Languages in Multilingual Dependency Parsing

Popis výsledku anglicky

UDify (Kondratyuk and Straka 2019) is a multilingual, multi-task parser fine-tuned on mBERT that achieves remarkable performance on high-resource languages. However, on some low-resource languages, its performance saturates early and decreases gradually as training proceeds. To address this issue, this study applies a data augmentation method to improve parsing performance. We conducted experiments on five few-shot and three zero-shot languages to test the effectiveness of this approach. The unlabeled attachment scores were improved on the zero-shot language dependency parsing tasks, with the average score increasing from 55.6% to 59.0%. Meanwhile, dependency parsing tasks in high-resource languages and other Universal Dependencies tasks were almost unaffected. The experimental results demonstrate that the data augmentation method is effective for low-resource languages in multilingual dependency parsing. Furthermore, our experiments confirm that continuously increasing the quantity of synthetic data enhances UDify's performance. This improvement was particularly effective for zero-shot target languages.

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

2025

Kód důvěrnosti údajů

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

Název periodika

Journal of Natural Language Processing

ISSN

2185-8314

e-ISSN

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Svazek periodika

32

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

33

Strana od-do

219-251

Kód UT WoS článku

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EID výsledku v databázi Scopus

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