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Jurnal SINTA 2

Cross-Lingual Transfer for Semantic Role Labeling in Indonesian

Journal of ICT Research and Applications (JICTRA) · Vol. 20 No. 2 (2026) · pp. 115–126

DOI
10.5614/itbj.ict.res.appl.2026.20.2.1
ISSN
2338-5499, 2337-5787
Published
2026-08-27
Publisher
DRI - ITB
Language
eng

Abstract

Semantic role labeling is a semantic analysis task that aims to identify the semantic relationships within a sentence, such as who did what to whom, where, when, and so on. Current semantic role labeling (SRL) models for the Indonesian language still face challenges in achieving strong performance due to the limited availability of annotated corpora, especially compared with English SRL models. Therefore, this paper develops an Indonesian SRL model using cross-lingual transfer. This approach addresses the data scarcity problem in Indonesian SRL by leveraging the availability of annotated English-language corpora. The method uses multilingual models and SRL datasets from both English and Indonesian. The multilingual models used in this study are XLM-R and mT5, both in base and large configurations. The datasets include Universal PropBank Indonesia and Gojali’s dataset for Indonesian, and CoNLL-2012 for English. Evaluation was conducted using test data from Universal PropBank Indonesia and Gojali’s dataset. Among all developed models, XLM-R large with cross-lingual transfer achieved the best performance, with an F1 score of 0.916 on Gojali’s dataset and 0.858 on the combined Universal PropBank Indonesia and Gojali datasets.

Keywords

Collaboration network of the authors

15 authors · 25 co-authorship links · 3 across different organisations

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  • Bandung Institute of Technology
  • Indonesia University of Education
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dwi Hendratmo WidyantoroBandung Institute of Technology4
Bambang Riyanto TrilaksonoBandung Institute of Technology3
E. Aminudin AzizIndonesia University of Education3
Muhammad Haris MaulanaBandung Institute of Technology1
Jan Wira Gotama PutraBandung Institute of Technology1
Ghoziyah Haitan RachmanBandung Institute of Technology1
Carmadi MachbubBandung Institute of Technology1
Wahyudi WahyudiBandung Institute of Technology1
Ary Setijadi PrihatmantoBandung Institute of Technology1
Benhard SitohangBandung Institute of Technology1
Gusti Ayu Putri SaptawatiBandung Institute of Technology1
Nasy`an Taufiq Al GhifariBandung Institute of Technology1
Richard ReinhartBandung Institute of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Cross-Lingual Transfer for Semantic Role Labeling in Indonesian</dc:title>
	<dc:creator>Haqi, Bariza</dc:creator>
	<dc:creator>Khodra, Masayu Leylia</dc:creator>
	<dc:subject xml:lang="en-US">cross-lingual transfer</dc:subject>
	<dc:subject xml:lang="en-US">multilingual language model</dc:subject>
	<dc:subject xml:lang="en-US">semantic role labeling</dc:subject>
	<dc:subject xml:lang="en-US">low-resource NLP</dc:subject>
	<dc:subject xml:lang="en-US">annotated corpora</dc:subject>
	<dc:subject xml:lang="en-US">CoNLL-2012</dc:subject>
	<dc:description xml:lang="en-US">Semantic role labeling is a semantic analysis task that aims to identify the semantic relationships within a sentence, such as who did what to whom, where, when, and so on. Current semantic role labeling (SRL) models for the Indonesian language still face challenges in achieving strong performance due to the limited availability of annotated corpora, especially compared with English SRL models. Therefore, this paper develops an Indonesian SRL model using cross-lingual transfer. This approach addresses the data scarcity problem in Indonesian SRL by leveraging the availability of annotated English-language corpora. The method uses multilingual models and SRL datasets from both English and Indonesian. The multilingual models used in this study are XLM-R and mT5, both in base and large configurations. The datasets include Universal PropBank Indonesia and Gojali’s dataset for Indonesian, and CoNLL-2012 for English. Evaluation was conducted using test data from Universal PropBank Indonesia and Gojali’s dataset. Among all developed models, XLM-R large with cross-lingual transfer achieved the best performance, with an F1 score of 0.916 on Gojali’s dataset and 0.858 on the combined Universal PropBank Indonesia and Gojali datasets.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2026-08-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/25768</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2026.20.2.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 20 No. 2 (2026); 115-126</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2026.20.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/25768/7458</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Journal of ICT Research and Applications</dc:rights>
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