Overcoming Early Saturation on 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%2F25%3AQLM25E7J" target="_blank" >RIV/00216208:11320/25:QLM25E7J - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.mwe-1.10.pdf" target="_blank" >https://aclanthology.org/2024.mwe-1.10.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2021.iwpt-1.9" target="_blank" >10.18653/v1/2021.iwpt-1.9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing

Popis výsledku v původním jazyce

UDify (Kondratyuk and Straka, 2019) is a multilingual and multi-task parser fine-tuned on mBERT that achieves remarkable performance in high-resource languages. However, the performance saturates early and decreases gradually in low-resource languages as training proceeds. This work applies a data augmentation method and conducts experiments on seven few-shot and four zero-shot languages. The unlabeled attachment scores were improved on the zero-shot languages dependency parsing tasks, with the average score rising from 67.1% to 68.7%. Meanwhile, dependency parsing tasks for high-resource languages and other tasks were hardly affected. Experimental results indicate the data augmentation method is effective for low-resource languages in a multilingual dependency parsing.

Název v anglickém jazyce

Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing

Popis výsledku anglicky

UDify (Kondratyuk and Straka, 2019) is a multilingual and multi-task parser fine-tuned on mBERT that achieves remarkable performance in high-resource languages. However, the performance saturates early and decreases gradually in low-resource languages as training proceeds. This work applies a data augmentation method and conducts experiments on seven few-shot and four zero-shot languages. The unlabeled attachment scores were improved on the zero-shot languages dependency parsing tasks, with the average score rising from 67.1% to 68.7%. Meanwhile, dependency parsing tasks for high-resource languages and other tasks were hardly affected. Experimental results indicate the data augmentation method is effective for low-resource languages in a multilingual dependency parsing.

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD)@ LREC-COLING 2024

ISBN

978-2-493-81420-3

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

63-69

Název nakladatele

ACL

Místo vydání

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Místo konání akce

Torino, Italia

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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