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EMNLP Findings2020

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

Jiyeon Ham, Yo Joong Choe, Kyubyong Park, Ilji Choi, Hyungjoon Soh

Abstract

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.

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Cited by top-tier papers6

  1. Making Monolingual Sentence Embeddings Multilingual using Knowledge DistillationNils Reimers, Iryna GurevychEMNLP 2020 · 54 citations
  2. Mitigating Language-Dependent Ethnic Bias in BERTJaimeen Ahn, Alice OhEMNLP 2021 · 5 citations
  3. CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme IngredientsJaehyung Seo, Hyeonseok Moon, Jaewook Lee, Sugyeong Eo et al.EMNLP 2023 · 1 citation
  4. Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual AlignmentYongxin Huang, Kexin Wang, Goran Glavas, Iryna GurevychACL 2025
  5. K-HALU: Multiple Answer Korean Hallucination Benchmark for Large Language ModelsJaehyung Seo, Heuiseok LimICLR 2025

Builds on1

  1. Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence ScoringSamuel Humeau, Kurt Shuster, Marie-Anne Lachaux, Jason WestonICLR 2020 · 316 citations

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