EMNLP2024
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, Lester James V. Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Ruben Antony Moniz, Muhammad Ravi Shulthan Habibi, Frederikus Hudi, Jann Railey Montalan, Ryan Hadiwijaya, Joanito Agili Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Amadeus Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Halim Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Adha Ryanda, Sonny Lazuardi Hermawan, Dan John Velasco, Muhammad Dehan Al Kautsar, Willy Fitra Hendria, Yasmin Moslem, Noah Flynn, Muhammad Farid Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Reza Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Ngee Tai Chia, Ayu Purwarianti, Sebastian Ruder, William-Chandra Tjhi, Peerat Limkonchotiwat, Alham Fikri Aji, Sedrick Keh, Genta Indra Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya
被引用 8 次
摘要
Southeast Asia (SEA) is a region characterized by rich linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, the performance of contemporary AI models for SEA languages is compromised by a significant lack of representation of texts, images, and auditory datasets from SEA. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the predominance of English training data, which raises concerns regarding potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub 1 to bridge the resource gap by providing standardized corpora and benchmarks 2 in nearly 1,000 SEA languages across three modalities. We assess the performance of AI models on 36 indigenous languages across 13 tasks included in SEACrowd, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate 1 https://seacrowd.github.io/seacrowd-catalogue/ 2 https://github.com/SEACrowd/seacrowd-datahub/ greater AI advancements, maximizing potential utility and resource equity for the future of AI in Southeast Asia.