Kobigbird-large: Transformation Of Transformer For Korean Language Understanding

Yang Kisu, Jang Yoonna, Lee Taewoo, Seong Jinwoo, Lee Hyungjin, Jang Hwanseok, Lim Heuiseok. Arxiv 2023

[Paper]    
Applications Model Architecture Pretraining Methods Training Techniques Transformer

This work presents KoBigBird-large, a large size of Korean BigBird that achieves state-of-the-art performance and allows long sequence processing for Korean language understanding. Without further pretraining, we only transform the architecture and extend the positional encoding with our proposed Tapered Absolute Positional Encoding Representations (TAPER). In experiments, KoBigBird-large shows state-of-the-art overall performance on Korean language understanding benchmarks and the best performance on document classification and question answering tasks for longer sequences against the competitive baseline models. We publicly release our model here.

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