IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
This addresses the slow progress in Indonesian NLP research by providing essential resources for benchmarking, though it is incremental as it adapts existing methods to a new language.
The authors tackled the lack of resources for Indonesian natural language processing by introducing IndoNLU, a benchmark with twelve diverse tasks and IndoBERT pre-trained models, resulting in the first comprehensive evaluation framework for Indonesian language understanding.
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.