SEACrowd：一個針對東南亞語言的多語言多模數據中樞和基準套件

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

June 14, 2024

作者: Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, Lester James V. Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno P. Kampman, Joel Ruben Antony Moniz, Muhammad Ravi Shulthan Habibi, Frederikus Hudi, Railey Montalan, Ryan Ignatius, Joanito Agili Lopo, William Nixon, Börje F. Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Amadeus, 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 Chia Tai, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Fikri Aji, Sedrick Keh, Genta Indra Winata, Ruochen Zhang, Fajri Koto, Zheng-Xin Yong, Samuel Cahyawijaya

cs.AI

摘要

東南亞（SEA）是一個語言多樣性和文化多樣性豐富的地區，擁有超過1,300種土著語言和6.71億人口。然而，現有的人工智慧模型在東南亞地區的文本、圖像和音頻數據集方面嚴重缺乏代表性，影響了針對該地區語言的人工智慧模型的質量。由於高質量數據集稀缺，再加上英語訓練數據的主導地位，評估東南亞語言模型具有挑戰性，引發對潛在文化誤代表的擔憂。為應對這些挑戰，我們引入SEACrowd，這是一個協作倡議，整合了一個全面的資源中心，通過提供標準化的語料庫，填補了近1,000種東南亞語言的資源空缺，涵蓋三種模式。通過我們的SEACrowd基準測試，我們評估了36種土著語言在13個任務上的人工智慧模型質量，為東南亞當前人工智慧格局提供了寶貴見解。此外，我們提出了促進更大人工智慧進步的策略，最大程度地發揮未來東南亞人工智慧的潛在效用和資源公平性。

English

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.