Papers by Garry Kuwanto
DUnE: Dataset for Unified Editing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models are susceptible to errors necessitating a comprehensive retraining process. |
| Approach: | They propose to define an edit as any natural language expression that solicits a change in the model’s outputs. |
| Outcome: | The proposed editing benchmarks show that retrieval-augmented language modeling outperforms specialized editing techniques and neither set of approaches has fully solved the generalized editing problem covered by the proposed benchmark. |
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025.naacl-long)
Copied to clipboard
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. |
Mitigating Translationese in Low-resource Languages: The Storyboard Approach (2024.lrec-main)
Copied to clipboard
Garry Kuwanto, Eno-Abasi E. Urua, Priscilla Amondi Amuok, Shamsuddeen Hassan Muhammad, Anuoluwapo Aremu, Verrah Otiende, Loice Emma Nanyanga, Teresiah W. Nyoike, Aniefon D. Akpan, Nsima Ab Udouboh, Idongesit Udeme Archibong, Idara Effiong Moses, Ifeoluwatayo A. Ige, Benjamin Ajibade, Olumide Benjamin Awokoya, Idris Abdulmumin, Saminu Mohammad Aliyu, Ruqayya Nasir Iro, Ibrahim Said Ahmad, Deontae Smith, Praise-EL Michaels, David Ifeoluwa Adelani, Derry Tanti Wijaya, Anietie Andy
| Challenge: | Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which introduce the translationese effect. |
| Approach: | They propose a method that uses storyboards to elicit more fluent and natural sentences from native speakers without direct exposure to the source text. |
| Outcome: | The proposed method compared with traditional translation-based methods in terms of accuracy and fluency. |