SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
The research paper titled "SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages" addresses a critical gap in AI model development for Southeast Asian (SEA) languages. With its introduction of SEACrowd, the work represents a collective effort to bolster resource availability and model evaluation capabilities across SEA languages, supporting the development of artificial intelligence technologies that are more representative of the region's linguistic diversity.
Despite SEA's linguistic richness, which features over 1,300 indigenous languages, the representation of these languages within existing AI datasets is severely inadequate. This lack of representation compromises the performance of machine learning models tailored to SEA languages, resulting in potential cultural bias and poor alignment with the nuanced societal contexts of SEA. Pre-training resources used for building machine learning models typically favor more widely used languages, predominantly English, thereby reinforcing a language imbalance and impeding equitable technological access in the SEA region.
The establishment of SEACrowd is an innovative approach to overcoming these challenges. SEACrowd consolidates approximately 500 corpora, covering nearly 1,000 SEA languages across text, image, and audio modalities, thereby providing a substantive foundation for future AI endeavors. Moreover, it presents a comprehensive benchmark suite for assessing the effectiveness of AI models, focusing on the quality of generated text in 36 indigenous languages over 13 distinct tasks.
Through SEACrowd, the paper emphasizes evaluating current AI models, revealing insights into their performance on SEA languages. Significant disparities in model capabilities are disclosed, particularly when models rely on Anglocentric corpora. These differences underscore the need for a locally grounded representation to reflect authentic cultural nuances better.
Crucial to future research is the prospect of enhancing cultural relevance in AI models by refining data quality and extending the accessibility of SEA language resources. SEACrowd offers a strategic pathway for this development by recommending collaboration between governments, industry leaders, and local communities to invest in data collection and language-specific pre-training activities. These actions should prioritize underrepresented languages with larger speaker populations, as well as minority languages at risk of marginalization.
Additionally, the paper challenges the prevalent issues of translationese in SEA text generation—a byproduct of machine translation that detracts from natural language authenticity. A classifier trained via SEACrowd's evaluation suite examines the generation quality in SEA languages, finding that existing models produce natural language output at variable success rates, often dependent on the volume and quality of language-specific data available.
SEACrowd's formulation represents a pivotal step toward narrowing the resource disparity and fostering more inclusive AI development in SEA. This initiative not only acts as an exemplar for regional data pool augmentation and enhanced model benchmarking but also outlines a framework for achieving equitable AI advancements that respect and integrate the vibrant linguistic and cultural tapestry of Southeast Asia. As AI continues to evolve, SEA can benefit significantly from such tailored technological progress, ensuring marginalized voices are acknowledged and preserved within the digital landscape. Further explorations into sustainable AI practices in the region remain essential for leveraging these advancements toward a more inclusive future.