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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Lovenia, Holy, Mahendra, Rahmad, Akbar, Salsabil Maulana, Miranda, Lester James V., Santoso, Jennifer, Aco, Elyanah, Fadhilah, Akhdan, Mansurov, Jonibek, Imperial, Joseph Marvin, Kampman, Onno P., Moniz, Joel Ruben Antony, Habibi, Muhammad Ravi Shulthan, Hudi, Frederikus, Montalan, Railey, Ignatius, Ryan, Lopo, Joanito Agili, Nixon, William, Karlsson, Börje F., Jaya, James, Diandaru, Ryandito, Gao, Yuze, Amadeus, Patrick, Wang, Bin, Cruz, Jan Christian Blaise, Whitehouse, Chenxi, Parmonangan, Ivan Halim, Khelli, Maria, Zhang, Wenyu, Susanto, Lucky, Ryanda, Reynard Adha, Hermawan, Sonny Lazuardi, Velasco, Dan John, Kautsar, Muhammad Dehan Al, Hendria, Willy Fitra, Moslem, Yasmin, Flynn, Noah, Adilazuarda, Muhammad Farid, Li, Haochen, Lee, Johanes, Damanhuri, R., Sun, Shuo, Qorib, Muhammad Reza, Djanibekov, Amirbek, Leong, Wei Qi, Do, Quyet V., Muennighoff, Niklas, Pansuwan, Tanrada, Putra, Ilham Firdausi, Xu, Yan, Tai, Ngee Chia, Purwarianti, Ayu, Ruder, Sebastian, Tjhi, William, Limkonchotiwat, Peerat, Aji, Alham Fikri, Keh, Sedrick, Winata, Genta Indra, Zhang, Ruochen, Koto, Fajri, Yong, Zheng-Xin, Cahyawijaya, Samuel
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
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_version_	1866909533154050048
author	Lovenia, Holy Mahendra, Rahmad Akbar, Salsabil Maulana Miranda, Lester James V. Santoso, Jennifer Aco, Elyanah Fadhilah, Akhdan Mansurov, Jonibek Imperial, Joseph Marvin Kampman, Onno P. Moniz, Joel Ruben Antony Habibi, Muhammad Ravi Shulthan Hudi, Frederikus Montalan, Railey Ignatius, Ryan Lopo, Joanito Agili Nixon, William Karlsson, Börje F. Jaya, James Diandaru, Ryandito Gao, Yuze Amadeus, Patrick Wang, Bin Cruz, Jan Christian Blaise Whitehouse, Chenxi Parmonangan, Ivan Halim Khelli, Maria Zhang, Wenyu Susanto, Lucky Ryanda, Reynard Adha Hermawan, Sonny Lazuardi Velasco, Dan John Kautsar, Muhammad Dehan Al Hendria, Willy Fitra Moslem, Yasmin Flynn, Noah Adilazuarda, Muhammad Farid Li, Haochen Lee, Johanes Damanhuri, R. Sun, Shuo Qorib, Muhammad Reza Djanibekov, Amirbek Leong, Wei Qi Do, Quyet V. Muennighoff, Niklas Pansuwan, Tanrada Putra, Ilham Firdausi Xu, Yan Tai, Ngee Chia Purwarianti, Ayu Ruder, Sebastian Tjhi, William Limkonchotiwat, Peerat Aji, Alham Fikri Keh, Sedrick Winata, Genta Indra Zhang, Ruochen Koto, Fajri Yong, Zheng-Xin Cahyawijaya, Samuel
author_facet	Lovenia, Holy Mahendra, Rahmad Akbar, Salsabil Maulana Miranda, Lester James V. Santoso, Jennifer Aco, Elyanah Fadhilah, Akhdan Mansurov, Jonibek Imperial, Joseph Marvin Kampman, Onno P. Moniz, Joel Ruben Antony Habibi, Muhammad Ravi Shulthan Hudi, Frederikus Montalan, Railey Ignatius, Ryan Lopo, Joanito Agili Nixon, William Karlsson, Börje F. Jaya, James Diandaru, Ryandito Gao, Yuze Amadeus, Patrick Wang, Bin Cruz, Jan Christian Blaise Whitehouse, Chenxi Parmonangan, Ivan Halim Khelli, Maria Zhang, Wenyu Susanto, Lucky Ryanda, Reynard Adha Hermawan, Sonny Lazuardi Velasco, Dan John Kautsar, Muhammad Dehan Al Hendria, Willy Fitra Moslem, Yasmin Flynn, Noah Adilazuarda, Muhammad Farid Li, Haochen Lee, Johanes Damanhuri, R. Sun, Shuo Qorib, Muhammad Reza Djanibekov, Amirbek Leong, Wei Qi Do, Quyet V. Muennighoff, Niklas Pansuwan, Tanrada Putra, Ilham Firdausi Xu, Yan Tai, Ngee Chia Purwarianti, Ayu Ruder, Sebastian Tjhi, William Limkonchotiwat, Peerat Aji, Alham Fikri Keh, Sedrick Winata, Genta Indra Zhang, Ruochen Koto, Fajri Yong, Zheng-Xin Cahyawijaya, Samuel
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2406_10118
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages Lovenia, Holy Mahendra, Rahmad Akbar, Salsabil Maulana Miranda, Lester James V. Santoso, Jennifer Aco, Elyanah Fadhilah, Akhdan Mansurov, Jonibek Imperial, Joseph Marvin Kampman, Onno P. Moniz, Joel Ruben Antony Habibi, Muhammad Ravi Shulthan Hudi, Frederikus Montalan, Railey Ignatius, Ryan Lopo, Joanito Agili Nixon, William Karlsson, Börje F. Jaya, James Diandaru, Ryandito Gao, Yuze Amadeus, Patrick Wang, Bin Cruz, Jan Christian Blaise Whitehouse, Chenxi Parmonangan, Ivan Halim Khelli, Maria Zhang, Wenyu Susanto, Lucky Ryanda, Reynard Adha Hermawan, Sonny Lazuardi Velasco, Dan John Kautsar, Muhammad Dehan Al Hendria, Willy Fitra Moslem, Yasmin Flynn, Noah Adilazuarda, Muhammad Farid Li, Haochen Lee, Johanes Damanhuri, R. Sun, Shuo Qorib, Muhammad Reza Djanibekov, Amirbek Leong, Wei Qi Do, Quyet V. Muennighoff, Niklas Pansuwan, Tanrada Putra, Ilham Firdausi Xu, Yan Tai, Ngee Chia Purwarianti, Ayu Ruder, Sebastian Tjhi, William Limkonchotiwat, Peerat Aji, Alham Fikri Keh, Sedrick Winata, Genta Indra Zhang, Ruochen Koto, Fajri Yong, Zheng-Xin Cahyawijaya, Samuel Computation and Language 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.
title	SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
topic	Computation and Language
url	https://arxiv.org/abs/2406.10118