Papers by Lucky Susanto
Monitoring Hate Speech in Indonesia: An NLP-based Classification of Social Media Texts (2024.emnlp-demo)
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| Challenge: | a lack of mechanisms to track the spread and severity of hate speech complicates the formulation of effective solutions. |
| Approach: | They have developed a universally robust hate speech classifier tailored for a narrower subset of texts that target vulnerable groups that have historically been the targets of hate speech in Indonesia. |
| Outcome: | The proposed tool has persuaded the General Election Supervisory Body in Indonesia (BAWASLU) to collaborate with the Alliance of Independent Journalists (AJI) to monitor hate speech in vulnerable areas in the country known for hate speech dissemination or hate-related violence in the upcoming Indonesian regional elections. |
A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information (2025.findings-acl)
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Lucky Susanto, Musa Izzanardi Wijanarko, Prasetia Anugrah Pratama, Zilu Tang, Fariz Akyas, Traci Hong, Ika Karlina Idris, Alham Fikri Aji, Derry Tanti Wijaya
| Challenge: | Prior research has focused on toxicity and polarization as separate problems . extreme polarizing deepens divisions, often leading to hostility and fragmentation . |
| Approach: | They propose to use a multi-label Indonesian dataset annotated for toxicity, polarization, and annotator demographic information to study polarizing language and toxicity. |
| Outcome: | The proposed dataset shows that polarization cues improve toxicity classification and vice versa. |
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025.naacl-long)
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Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha, Rifki Afina Putri, Wang Yutong, Adam Nohejl, Ubaidillah Ariq Prathama, Nedjma Ousidhoum, Afifa Amriani, Anar Rzayev, Anirban Das, Ashmari Pramodya, Aulia Adila, Bryan Wilie, Candy Olivia Mawalim, Cheng Ching Lam, Daud Abolade, Emmanuele Chersoni, Enrico Santus, Fariz Ikhwantri, Garry Kuwanto, Hanyang Zhao, Haryo Akbarianto Wibowo, Holy Lovenia, Jan Christian Blaise Cruz, Jan Wira Gotama Putra, Junho Myung, Lucky Susanto, Maria Angelica Riera Machin, Marina Zhukova, Michael Anugraha, Muhammad Farid Adilazuarda, Natasha Christabelle Santosa, Peerat Limkonchotiwat, Raj Dabre, Rio Alexander Audino, Samuel Cahyawijaya, Shi-Xiong Zhang, Stephanie Yulia Salim, Yi Zhou, Yinxuan Gui, David Ifeoluwa Adelani, En-Shiun Annie Lee, Shogo Okada, Ayu Purwarianti, Alham Fikri Aji, Taro Watanabe, Derry Tanti Wijaya, Alice Oh, Chong-Wah Ngo
| Challenge: | Vision Language Models struggle with cultural-specific knowledge, especially in languages other than English and in underrepresented cultural contexts. |
| Approach: | They propose a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects and a training dataset. |
| Outcome: | The proposed model performs better with correct location context, but struggles with adversarial contexts and predicting specific regional cuisines and languages. |
What Do Indonesians Really Need from Language Technology? A Nationwide Survey (2025.emnlp-main)
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| Challenge: | Despite efforts to develop NLP for Indonesia’s 700+ local languages, progress remains costly due to the need for direct engagement with native speakers. |
| Approach: | They conduct a nationwide survey to assess the actual needs of native Indonesian speakers. |
| Outcome: | The findings indicate that addressing language barriers is the most critical priority . concerns around privacy, bias, and the use of public data highlight the need for greater transparency and clear communication to support broader AI adoption. |
NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts (2025.acl-long)
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Muhammad Farid Adilazuarda, Musa Izzanardi Wijanarko, Lucky Susanto, Khumaisa Nur’aini, Derry Tanti Wijaya, Alham Fikri Aji
| Challenge: | NusaAksara covers 8 scripts across 7 languages, including low-resource languages not commonly seen in NLP benchmarks. |
| Approach: | They propose a benchmark for Indonesian scripts that includes their original scripts and a dataset that includes 8 scripts across 7 languages. |
| Outcome: | The proposed benchmark covers 8 scripts across 7 languages, including low-resource languages not commonly seen in NLP benchmarks. |
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)
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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya
| Challenge: | Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA . |
| Approach: | They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities. |
| Outcome: | a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region . |