Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ASNZLRUMG" target="_blank" >RIV/00216208:11320/22:SNZLRUMG - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IALP57159.2022.9961296" target="_blank" >https://doi.org/10.1109/IALP57159.2022.9961296</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IALP57159.2022.9961296" target="_blank" >10.1109/IALP57159.2022.9961296</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

Popis výsledku v původním jazyce

In recent years, cross-lingual transfer learning has been gaining positive trends across NLP tasks. This research aims to develop a dependency parser for Indonesian using cross-lingual transfer learning. The dependency parser uses a Transformer as the encoder layer and a deep biaffine attention decoder as the decoder layer. The model is trained using a transfer learning approach from a source language to our target language with fine-tuning. We choose four languages as the source domain for comparison: French, Italian, Slovenian, and English. Our proposed approach is able to improve the performance of the dependency parser model for Indonesian as the target domain on both same-domain and cross-domain testing. Compared to the baseline model, our best model increases UAS up to 4.31% and LAS up to 4.46%. Among the chosen source languages of dependency treebanks, French and Italian that are selected based on LangRank output perform better than other languages selected based on other criteria. French, which has the highest rank from LangRank, performs the best on cross-lingual transfer learning for the dependency parser model.

Název v anglickém jazyce

Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

Popis výsledku anglicky

In recent years, cross-lingual transfer learning has been gaining positive trends across NLP tasks. This research aims to develop a dependency parser for Indonesian using cross-lingual transfer learning. The dependency parser uses a Transformer as the encoder layer and a deep biaffine attention decoder as the decoder layer. The model is trained using a transfer learning approach from a source language to our target language with fine-tuning. We choose four languages as the source domain for comparison: French, Italian, Slovenian, and English. Our proposed approach is able to improve the performance of the dependency parser model for Indonesian as the target domain on both same-domain and cross-domain testing. Compared to the baseline model, our best model increases UAS up to 4.31% and LAS up to 4.46%. Among the chosen source languages of dependency treebanks, French and Italian that are selected based on LangRank output perform better than other languages selected based on other criteria. French, which has the highest rank from LangRank, performs the best on cross-lingual transfer learning for the dependency parser model.

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 International Conference on Asian Language Processing (IALP)

ISBN

978-1-66547-674-4

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

488-493

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Singapore, Singapore

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000896159700083