# A Survey on Awesome Korean NLP Datasets

arXiv: https://arxiv.org/abs/2112.01624 · HTML: https://mlsanity.com/paper/2112.01624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 71 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers](https://mlsanity.com/paper/2109.04650/) (2021)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019)
- [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://mlsanity.com/paper/2005.12503/) (2020)
- [KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding](https://mlsanity.com/paper/2004.03289/) (2020)
- [An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks](https://mlsanity.com/paper/2010.02534/) (2020)
- [Semantic Modelling with Long-Short-Term Memory for Information Retrieval](https://mlsanity.com/paper/1412.6629/) (2014)
- [ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers](https://mlsanity.com/paper/2004.09367/) (2020)
- [Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models](https://mlsanity.com/paper/2108.06665/) (2021)
- [KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding](https://mlsanity.com/paper/2101.11363/) (2021)
- [Kosp2e: Korean Speech to English Translation Corpus](https://mlsanity.com/paper/2107.02875/) (2021)
- [Open Korean Corpora: A Practical Report](https://mlsanity.com/paper/2012.15621/) (2020)
- [A Sub-Character Architecture for Korean Language Processing](https://mlsanity.com/paper/1707.06341/) (2017)
- [StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands](https://mlsanity.com/paper/2103.13439/) (2021)
- [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://mlsanity.com/paper/1912.00342/) (2019)
- [LSTM-Based Predictions for Proactive Information Retrieval](https://mlsanity.com/paper/1606.06137/) (2016)
- [Investigating an Effective Character-level Embedding in Korean Sentence Classification](https://mlsanity.com/paper/1905.13656/) (2019)
- [A novel hybrid methodology of measuring sentence similarity](https://mlsanity.com/paper/2105.00648/) (2021)
- [CareCall: a Call-Based Active Monitoring Dialog Agent for Managing COVID-19 Pandemic](https://mlsanity.com/paper/2007.02642/) (2020)

## Built on by (most cited first, 3 of 3)

- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models](https://mlsanity.com/paper/2209.06422/) (2022)
- [KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering](https://mlsanity.com/paper/2407.00342/) (2024)

## Abstract (arXiv)

English based datasets are commonly available from Kaggle, GitHub, or recently published papers. Although benchmark tests with English datasets are sufficient to show off the performances of new models and methods, still a researcher need to train and validate the models on Korean based datasets to produce a technology or product, suitable for Korean processing. This paper introduces 15 popular Korean based NLP datasets with summarized details such as volume, license, repositories, and other research results inspired by the datasets. Also, I provide high-resolution instructions with sample or statistics of datasets. The main characteristics of datasets are presented on a single table to provide a rapid summarization of datasets for researchers.