Wilson Wongso

Wilson Wongso

CS PhD Cand. @ UNSW; Researcher @ LazarusNLP; MLE @ Bookbot;

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portfolio

publications

Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures

Published in 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2021

Most natural language understanding breakthroughs occur in popularly spoken languages, while low-resource languages are rarely examined. We pre-trained as well as compared different Transformer-based architectures on the Javanese language. They were trained on causal and masked language modeling tasks, with Javanese Wikipedia documents as corpus, and could then be fine-tuned to downstream natural language understanding tasks. To speed up pre-training, we transferred English word-embeddings, utilized gradual unfreezing of layers, and applied discriminative fine-tuning. We further fine-tuned our models to classify binary movie reviews and find that they were on par with multilingual/cross-lingual Transformers. We release our pre-trained models for others to use, in hopes of encouraging other researchers to work on low-resource languages like Javanese.

Recommended citation: W. Wongso, D. S. Setiawan and D. Suhartono, "Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures," 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia, 2021, pp. 1-7, doi: 10.1109/ICACSIS53237.2021.9631331.
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Pre-trained transformer-based language models for Sundanese

Published in Journal of Big Data, 2022

The Sundanese language has over 32 million speakers worldwide, but the language has reaped little to no benefits from the recent advances in natural language understanding. Like other low-resource languages, the only alternative is to fine-tune existing multilingual models. In this paper, we pre-trained three monolingual Transformer-based language models on Sundanese data. When evaluated on a downstream text classification task, we found that most of our monolingual models outperformed larger multilingual models despite the smaller overall pre-training data. In the subsequent analyses, our models benefited strongly from the Sundanese pre-training corpus size and do not exhibit socially biased behavior. We released our models for other researchers and practitioners to use.

Recommended citation: Wongso, W., Lucky, H. & Suhartono, D. "Pre-trained transformer-based language models for Sundanese." J Big Data 9, 39 (2022). https://doi.org/10.1186/s40537-022-00590-7
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Many-to-Many Multilingual Translation Model for Languages of Indonesia

Published in IEEE Access, 2023

Indonesia is home to over 700 languages and most people speak their respective regional languages aside from the lingua franca. In this paper, we focus on the task of multilingual machine translation for 45 regional Indonesian languages and introduced Indo-T5 which leveraged the mT5 sequence-to-sequence language model as a baseline. Performances of bilingual and multilingual fine-tuning methods were also compared, in which we found that our models have outperformed current state-of-the-art translation models. We also investigate the use of religious texts from the Bible as an intermediate mid-resource translation domain for low-resource translation domain specialization. Our findings suggest that this two-step fine-tuning approach is highly effective in improving the quality of translations for low-resource text domains. Our results show an increase in SacreBLEU scores when evaluated on the low-resource NusaX dataset. We release our translation models for other researchers to leverage.

Recommended citation: Wongso, W., Joyoadikusumo, A., Buana, B. S., & Suhartono, D. (2023). Many-to-Many Multilingual Translation Model for Languages of Indonesia. IEEE Access.
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IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

Published in IEEE Access, 2024

Sarcasm detection in the Indonesian language poses a unique set of challenges due to the linguistic nuances and cultural specificities of the Indonesian social media landscape. Understanding the dynamics of sarcasm in this context requires a deep dive into language patterns and the socio-cultural background that shapes the use of sarcasm as a form of criticism and expression. In this study, we developed the first publicly available Indonesian sarcasm detection benchmark datasets from social media texts. We extensively investigated the results of classical machine learning algorithms, pre-trained language models, and recent large language models (LLMs). Our findings show that fine-tuning pre-trained language models is still superior to other techniques, achieving F1 scores of 62.74% and 76.92% on the Reddit and Twitter subsets respectively. Further, we show that recent LLMs fail to perform zero-shot classification for sarcasm detection and that tackling data imbalance requires a more sophisticated data augmentation approach than our basic methods.

Recommended citation: Suhartono, D., Wongso, W., & Handoyo, A. T. (2024). IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection. IEEE Access.
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GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems

Published in arXiv, 2024

Traditional POI recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings. Furthermore, the cold-start problem – where systems have insufficient data for new users – limits their ability to generate accurate recommendations. Existing methods often address this by leveraging similar trajectories from other users, but this approach can be computationally expensive and increases the context length for LLM-based methods, making them difficult to scale. To address these limitations, we propose a method that generates natural language (NL) user profiles from large-scale, location-based social network (LBSN) check-ins, utilizing robust personality assessments and behavioral theories. These NL profiles capture user preferences, routines, and behaviors, improving POI prediction accuracy while offering enhanced transparency. By incorporating NL profiles as system prompts to LLMs, our approach reduces reliance on extensive historical data, while remaining flexible, easily updated, and computationally efficient. Our method is not only competitive with other LLM-based and complex agentic frameworks but is also more scalable for real-world scenarios and on-device POI recommendations. Results demonstrate that our approach consistently outperforms baseline methods, offering a more interpretable and resource-efficient solution for POI recommendation systems.

Recommended citation: Wongso, W., Xue, H., & Salim, F. D. (2024). GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems. arXiv preprint arXiv:2410.20643.
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NusaBERT Teaching IndoBERT to be Multilingual and Multicultural

Published in Proceedings of the Second Workshop in South East Asian Language Processing, 2025

Indonesia’s linguistic landscape is remarkably diverse, encompassing over 700 languages and dialects, making it one of the world’s most linguistically rich nations. This diversity, coupled with the widespread practice of code-mixing and the presence of low-resource regional languages, presents unique challenges for modern pre-trained language models. In response to these challenges, we developed NusaBERT, building upon IndoBERT by incorporating vocabulary expansion and leveraging a diverse multilingual corpus that includes regional languages. Through rigorous evaluation across a range of benchmarks, NusaBERT demonstrates state-of-the-art performance in tasks involving multiple languages of Indonesia, paving the way for future natural language understanding research for under-represented languages.

Recommended citation: Wilson Wongso, David Samuel Setiawan, Steven Limcorn, and Ananto Joyoadikusumo. 2025. NusaBERT: Teaching IndoBERT to be Multilingual and Multicultural. In Proceedings of the Second Workshop in South East Asian Language Processing, pages 10–26, Online. Association for Computational Linguistics.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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