Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

Andhika Yusup Maulana, Ika Alfina, Kurniawati Azizah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2022 International Conference on Asian Language Processing, IALP 2022
EditorsRong Tong, Yanfeng Lu, Minghui Dong, Wengao Gong, Haizhou Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages488-493
Number of pages6
ISBN (Electronic)9781665476744
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Asian Language Processing, IALP 2022 - Singapore, Singapore
Duration: 27 Oct 202228 Oct 2022

Publication series

Name2022 International Conference on Asian Language Processing, IALP 2022

Conference

Conference2022 International Conference on Asian Language Processing, IALP 2022
Country/TerritorySingapore
CitySingapore
Period27/10/2228/10/22

Keywords

  • cross-domain
  • cross-lingual transfer learning
  • dependency parser
  • transformer

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