Papers by Ayu Purwarianti
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models (2025.coling-main)
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| Challenge: | Existing methods for creating a vision question-answering with natural language explanations rely on human annotations that are time-consuming and costly. |
| Approach: | They propose a method that generates high-quality natural language explanations using LVLMs by using visual prompts. |
| Outcome: | The proposed method generates high-quality synthetic VQA-NLE datasets 20x faster than human annotations with minimal decrease in qualitative metrics. |
Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian (2023.emnlp-main)
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| Challenge: | Ambiguity is one of the challenges in natural language processing. |
| Approach: | They propose to resolve structurally ambiguous sentences into unambiguous texts in Indonesian using prosodic information. |
| Outcome: | The proposed system achieves a disambiguation accuracy of 79.6% while the proposed direct system yields an even more impressive disambiguations accuracy of 82%. |
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation (2021.emnlp-main)
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Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Khodra, Ayu Purwarianti, Pascale Fung
| Challenge: | Lack of publicly available NLG benchmarks for low-resource languages poses a challenge . authors show that IndoBART and IndoGPT achieve competitive performance on all tasks . |
| Approach: | They propose a benchmark to measure natural language generation progress in three low-resource languages of Indonesia . they use a corpus of pretraining datasets to build their models . |
| Outcome: | The proposed benchmark measures progress in Indonesian, Javanese, and Sundanese . the results highlight the importance of pretraining on closely related, localized languages . |
NusaCrowd: Open Source Initiative for Indonesian NLP Resources (2023.findings-acl)
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Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Winata, Bryan Wilie, Fajri Koto, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Jennifer Santoso, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Muhammad Satrio Wicaksono, Ivan Parmonangan, Ika Alfina, Ilham Firdausi Putra, Samsul Rahmadani, Yulianti Oenang, Ali Septiandri, James Jaya, Kaustubh Dhole, Arie Suryani, Rifki Afina Putri, Dan Su, Keith Stevens, Made Nindyatama Nityasya, Muhammad Adilazuarda, Ryan Hadiwijaya, Ryandito Diandaru, Tiezheng Yu, Vito Ghifari, Wenliang Dai, Yan Xu, Dyah Damapuspita, Haryo Wibowo, Cuk Tho, Ichwanul Karo Karo, Tirana Fatyanosa, Ziwei Ji, Graham Neubig, Timothy Baldwin, Sebastian Ruder, Pascale Fung, Herry Sujaini, Sakriani Sakti, Ayu Purwarianti
| Challenge: | Existing NLP research in Indonesian languages has been held back by factors such as language diversity, orthographic variation, resource limitation and other societal challenges. |
| Approach: | They present a collaborative initiative to collect and unify existing resources for Indonesian languages and open access to previously non-public resources. |
| Outcome: | The results show that the datasets are highly reliable and can be used to generate the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. |
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. |
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization (2024.findings-emnlp)
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| Challenge: | Pretrained language models have shown remarkable generalization toward multiple tasks and languages, but their generalization towards unseen languages is poor. |
| Approach: | They propose a regularization technique that incorporates various aspects of languages to better characterize linguistics constraints. |
| Outcome: | The proposed technique improves accuracy of mBERT and XLM-R on unseen languages by 18% and 2% compared to fully finetuned models. |
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding (2020.aacl-main)
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Bryan Wilie, Karissa Vincentio, Genta Indra Winata, Samuel Cahyawijaya, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti
| Challenge: | Despite the availability of data on Indonesian, progress on this language is slow . available datasets are scattered, with a lack of documentation and minimal community engagement. |
| Approach: | They propose a resource for training, evaluation, and benchmarking on Indonesian natural language understanding tasks. |
| Outcome: | The proposed resource includes 12 tasks ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. |
MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding (2025.findings-naacl)
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| Challenge: | Multi-Layer Key-Value (MLKV) sharing reduces memory usage by 6x compared to Multi-Query Attention and Grouped-Query Attributes. |
| Approach: | They propose a novel approach that extends KV sharing across transformer layers to reduce memory usage beyond what was possible with Multi-Query Attention and Grouped-Query Attributes. |
| Outcome: | The proposed approach reduces KV cache size by 6x with minimal performance loss and scales linearly with model size, batch size, and sequence length. |
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 . |