# KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding

arXiv: https://arxiv.org/abs/2004.03289 · HTML: https://mlsanity.com/paper/2004.03289/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 30 of 37)

- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://mlsanity.com/paper/2402.11548/) (2024)
- [Mitigating Language-Dependent Ethnic Bias in BERT](https://mlsanity.com/paper/2109.05704/) (2021)
- [CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean](https://mlsanity.com/paper/2403.06412/) (2024)
- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models](https://mlsanity.com/paper/2309.02706/) (2023)
- [KOBEST: Korean Balanced Evaluation of Significant Tasks](https://mlsanity.com/paper/2204.04541/) (2022)
- [ScandEval: A Benchmark for Scandinavian Natural Language Processing](https://mlsanity.com/paper/2304.00906/) (2023)
- [Compositional Evaluation on Japanese Textual Entailment and Similarity](https://mlsanity.com/paper/2208.04826/) (2022)
- [Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models](https://mlsanity.com/paper/2108.06665/) (2021)
- [Where Are We? Evaluating LLM Performance on African Languages](https://mlsanity.com/paper/2502.19582/) (2025)
- [KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding](https://mlsanity.com/paper/2101.11363/) (2021)
- [Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks](https://mlsanity.com/paper/2101.10649/) (2021)
- [Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications](https://mlsanity.com/paper/2403.17528/) (2024)
- [SLM as Guardian: Pioneering AI Safety with Small Language Models](https://mlsanity.com/paper/2405.19795/) (2024)
- [GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2305.06545/) (2023)
- [A Survey on Awesome Korean NLP Datasets](https://mlsanity.com/paper/2112.01624/) (2021)
- [InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples](https://mlsanity.com/paper/2210.03068/) (2022)
- [Are ELECTRA's Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity](https://mlsanity.com/paper/2402.13130/) (2024)
- [XNLIeu: a dataset for cross-lingual NLI in Basque](https://mlsanity.com/paper/2404.06996/) (2024)
- [A novel hybrid methodology of measuring sentence similarity](https://mlsanity.com/paper/2105.00648/) (2021)
- [Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment](https://mlsanity.com/paper/2407.14878/) (2024)
- [Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress](https://mlsanity.com/paper/2112.03014/) (2021)
- [JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications](https://mlsanity.com/paper/2301.08193/) (2023)
- [Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity](https://mlsanity.com/paper/2403.05257/) (2024)
- [Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generation](https://mlsanity.com/paper/2503.09094/) (2025)
- [Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks?](https://mlsanity.com/paper/2507.20419/) (2025)
- [ViLegalNLI: Natural Language Inference for Vietnamese Legal Texts](https://mlsanity.com/paper/2605.00116/) (2026)
- [Structured Pattern Pruning Using Regularization](https://mlsanity.com/paper/2109.08814/) (2021)
- [DPRK-BERT: The Supreme Language Model](https://mlsanity.com/paper/2112.00567/) (2021)

## Abstract (arXiv)

Natural language inference (NLI) and semantic textual similarity (STS) are key tasks in natural language understanding (NLU). Although several benchmark datasets for those tasks have been released in English and a few other languages, there are no publicly available NLI or STS datasets in the Korean language. Motivated by this, we construct and release new datasets for Korean NLI and STS, dubbed KorNLI and KorSTS, respectively. Following previous approaches, we machine-translate existing English training sets and manually translate development and test sets into Korean. To accelerate research on Korean NLU, we also establish baselines on KorNLI and KorSTS. Our datasets are publicly available at https://github.com/kakaobrain/KorNLUDatasets.