Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress
It provides a comprehensive review for researchers and practitioners working on Korean natural language processing, but it is incremental as it focuses on summarizing existing models rather than introducing new methods.
This paper surveys and compares various Korean pretrained language models (PLMs) developed over the past three years, analyzing them numerically and qualitatively to assess their progress and performance.
With the advent of Transformer, which was used in translation models in 2017, attention-based architectures began to attract attention. Furthermore, after the emergence of BERT, which strengthened the NLU-specific encoder part, which is a part of the Transformer, and the GPT architecture, which strengthened the NLG-specific decoder part, various methodologies, data, and models for learning the Pretrained Language Model began to appear. Furthermore, in the past three years, various Pretrained Language Models specialized for Korean have appeared. In this paper, we intend to numerically and qualitatively compare and analyze various Korean PLMs released to the public.