SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for
  Southeast Asian Languages
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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SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for
  Southeast Asian Languages
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.
SEACrowd consolidates a comprehensive resource hub and benchmarks for evaluating AI models across nearly 1,000 Southeast Asian languages to address the lack of representation and improve cultural sensitivity in AI.
SEACrowd standardized corpora AI models benchmarks indigenous languages