Tanrada Pansuwan


2024

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SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
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
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, through a collaborative movement, we introduce SEACrowd, a comprehensive resource center that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in Southeast Asia.