SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for
  Southeast Asian Languages
Authors: name: Holy Lovenia; name: Rahmad Mahendra; name: Salsabil Maulana Akbar; name: Lester James V. Miranda; name: Jennifer Santoso; name: Elyanah Aco; name: Akhdan Fadhilah; name: Jonibek Mansurov; name: Joseph Marvin Imperial; name: Onno P. Kampman; name: Joel Ruben Antony Moniz; name: Muhammad Ravi Shulthan Habibi; name: Frederikus Hudi; name: Railey Montalan; name: Ryan Ignatius; name: Joanito Agili Lopo; name: William Nixon; name: Börje F. Karlsson; name: James Jaya; name: Ryandito Diandaru; name: Yuze Gao; name: Patrick Amadeus; name: Bin Wang; name: Jan Christian Blaise Cruz; name: Chenxi Whitehouse; name: Ivan Halim Parmonangan; name: Maria Khelli; name: Wenyu Zhang; name: Lucky Susanto; name: Reynard Adha Ryanda; name: Sonny Lazuardi Hermawan; name: Dan John Velasco; name: Muhammad Dehan Al Kautsar; name: Willy Fitra Hendria; name: Yasmin Moslem; name: Noah Flynn; name: Muhammad Farid Adilazuarda; name: Haochen Li; name: Johanes Lee; name: R. Damanhuri; name: Shuo Sun; name: Muhammad Reza Qorib; name: Amirbek Djanibekov; name: Wei Qi Leong; name: Quyet V. Do; name: Niklas Muennighoff; name: Tanrada Pansuwan; name: Ilham Firdausi Putra; name: Yan Xu; name: Ngee Chia Tai; name: Ayu Purwarianti; name: Sebastian Ruder; name: William Tjhi; name: Peerat Limkonchotiwat; name: Alham Fikri Aji; name: Sedrick Keh; name: Genta Indra Winata; name: Ruochen Zhang; name: Fajri Koto; name: Zheng-Xin Yong; name: Samuel Cahyawijaya
Published: 2024-06-14 15:23:39

Abstract: 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, we introduce SEACrowd, a
collaborative initiative that consolidates a comprehensive resource hub 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 SEA.