KLUE: Korean Language Understanding Evaluation

Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)

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Authors

Sungjoon Park, Jihyung Moon, Sungdong Kim, Won Ik Cho, Ji Yoon Han, Jangwon Park, Chisung Song, Junseong Kim, Youngsook Song, Taehwan Oh, Joohong Lee, Juhyun Oh, Sungwon Lyu, Younghoon Jeong, Inkwon Lee, Sangwoo Seo, Dongjun Lee, Hyunwoo Kim, Myeonghwa Lee, Seongbo Jang, Seungwon Do, Sunkyoung Kim, Kyungtae Lim, Jongwon Lee, Kyumin Park, Jamin Shin, Seonghyun Kim, Lucy Park, Lucy Park, Alice Oh, Jung-Woo Ha (NAVER AI Lab), Kyunghyun Cho, Kyunghyun Cho

Abstract

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of eight Korean natural language understanding (NLU) tasks, including Topic Classification, Semantic Textual Similarity, Natural LanguageInference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. We create all of the datasets from scratch in a principled way. We design the tasks to have diverse formats and each task to be built upon various source corpora that respect copyrights. Also, we propose suitable evaluation metrics and organize annotation protocols in a way to ensure quality. To prevent ethical risks in KLUE, we proactively remove examples reflecting social biases, containing toxic content or personally identifiable information (PII). Along with the benchmark datasets, we release pre-trained language models (PLM) for Korean, KLUE-BERT and KLUE-RoBERTa, and find KLUE-Roberta-large outperforms other baselines including multilingual PLMs and existing open-source Korean PLMs. The fine-tuning recipes are publicly open for anyone to reproduce our baseline result. We believe our work will facilitate future research on cross-lingual as well as Korean language models and the creation of similar resources for other languages. KLUE is available at https://klue-benchmark.com.