Papers by Rifki Afina Putri
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. |
Rethinking Annotation: Can Language Learners Contribute? (2023.acl-long)
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| Challenge: | Researchers have traditionally recruited native speakers to provide annotations for benchmark datasets, but there are languages for which recruiting native speakers is difficult. |
| Approach: | They recruit 36 language learners and provide two types of additional resources and perform mini-tests to measure their language proficiency. |
| Outcome: | The proposed method improves learners' language proficiency in terms of vocabulary and grammar. |
AnnoHID: LLM-Assisted Annotation Framework for Low-Resource Medical Texts (2026.acl-demo)
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Annisa Maulida Ningtyas, Guntur Budi Herwanto, Yunita Sari, Rifki Afina Putri, Filip Kovacevic, Alaa El-Ebshihy, Varvara Arzt, Florina Piroi
| Challenge: | Social media platforms are a popular way to communicate with medical experts and improve health literacy. |
| Approach: | They introduce a semi-automated annotation framework for medical texts in low-resource languages . they use large language models for pre-annotation and human validation to support efficient annotation . |
| Outcome: | The proposed framework is applied to medical social media texts in Bahasa Indonesia . it yields higher inter-annotator agreement and human review improves output . future work focuses on mitigating pre-annotation bias and reducing annotation overhead . |
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension (2022.emnlp-main)
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| Challenge: | Existing MRC datasets in Indonesian are inadequate because of the small size and limited question types. |
| Approach: | They propose to combine automatic and manual unanswerable question generation to minimize the cost of manual dataset construction while maintaining the dataset quality. |
| Outcome: | The proposed dataset significantly improves the performance of Indonesian MRC models, showing a large improvement for unanswerable questions. |