Scoping natural language processing in Indonesian and Malay for education applications

Zara Maxwell-Smith, Michelle Kohler, Hanna Suominen


Abstract
Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find. A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects. Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work. We charted 657 included studies according to Hirschberg and Manning’s 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis. In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay.
Anthology ID:
2022.acl-srw.15
Original:
2022.acl-srw.15v1
Version 2:
2022.acl-srw.15v2
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Samuel Louvan, Andrea Madotto, Brielen Madureira
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
171–228
Language:
URL:
https://aclanthology.org/2022.acl-srw.15
DOI:
10.18653/v1/2022.acl-srw.15
Bibkey:
Cite (ACL):
Zara Maxwell-Smith, Michelle Kohler, and Hanna Suominen. 2022. Scoping natural language processing in Indonesian and Malay for education applications. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 171–228, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Scoping natural language processing in Indonesian and Malay for education applications (Maxwell-Smith et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-srw.15.pdf
Data
IndoNLU Benchmark