Nepali Dependency Parsing Using Transfer Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A6MQCRAW5" target="_blank" >RIV/00216208:11320/25:6MQCRAW5 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214108638&doi=10.1007%2f978-981-97-8666-4_15&partnerID=40&md5=ffb189c4bee648c546cf98f15360a9c8" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214108638&doi=10.1007%2f978-981-97-8666-4_15&partnerID=40&md5=ffb189c4bee648c546cf98f15360a9c8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-97-8666-4_15" target="_blank" >10.1007/978-981-97-8666-4_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Nepali Dependency Parsing Using Transfer Learning

Popis výsledku v původním jazyce

This paper presents a dependency parser developed for the low resource Nepali language along with the creation of annotated dataset conforming to Universal Dependencies treebank. The created dataset is used to train along with other languages to create a neural dependency parser based on graph-based parsing. Training is done in the state-of-the art graph-based neural architecture that makes use of multilingual BERT embeddings. Various experiments have been conducted varying the source training languages and dataset sizes. In zero-shot case for Nepali language the UAS and LAS scores obtained are 59.52 and 47.47 respectively, whereas for few-shot case the scores are 80.73 and 72.67 respectively. This work presents a good enough baseline as well as high quality data that can now be used for further research in the direction of dependency parsing for Nepali language. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Název v anglickém jazyce

Nepali Dependency Parsing Using Transfer Learning

Popis výsledku anglicky

This paper presents a dependency parser developed for the low resource Nepali language along with the creation of annotated dataset conforming to Universal Dependencies treebank. The created dataset is used to train along with other languages to create a neural dependency parser based on graph-based parsing. Training is done in the state-of-the art graph-based neural architecture that makes use of multilingual BERT embeddings. Various experiments have been conducted varying the source training languages and dataset sizes. In zero-shot case for Nepali language the UAS and LAS scores obtained are 59.52 and 47.47 respectively, whereas for few-shot case the scores are 80.73 and 72.67 respectively. This work presents a good enough baseline as well as high quality data that can now be used for further research in the direction of dependency parsing for Nepali language. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

8th International Conference on Information System Design and Intelligent Applications

ISBN

978-981978665-7

ISSN

2367-3370

e-ISSN

—

Počet stran výsledku

12

Strana od-do

179-190

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Dubai

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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