Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in the natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for the training, evaluating, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset Indo4B collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, and thus it enables everyone to benchmark their system performances.
@article{arxiv.2009.05387,
title = {IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author = {Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and Xiaohong Li and Zhi Yuan Lim and Sidik Soleman and Rahmad Mahendra and Pascale Fung and Syafri Bahar and Ayu Purwarianti},
journal= {arXiv preprint arXiv:2009.05387},
year = {2020}
}
Comments
This paper will be presented in AACL-IJCNLP 2020 (with new results and acknowledgment)