title: SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for
    Southeast Asian Languages
summary: 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.
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paper_title: SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for
    Southeast Asian Languages
paper_summary: 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.
paper_ai_keywords: SEACrowd standardized corpora SEACrowd benchmarks