Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

—

Publication year

2022

Confidentiality

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

Article name in the collection

2022 International Conference on Asian Language Processing (IALP)

ISBN

978-1-66547-674-4

ISSN

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e-ISSN

—

Number of pages

6

Pages from-to

488-493

Publisher name

IEEE

Place of publication

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Event location

Singapore, Singapore

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000896159700083