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.
Anthology ID:
2020.findings-emnlp.39
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
422–430
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.39
DOI:
10.18653/v1/2020.findings-emnlp.39
Bibkey:
Cite (ACL):
Jiyeon Ham, Yo Joong Choe, Kyubyong Park, Ilji Choi, and Hyungjoon Soh. 2020. KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 422–430, Online. Association for Computational Linguistics.
Cite (Informal):
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding (Ham et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.39.pdf
Code
 kakaobrain/KorNLUDatasets +  additional community code
Data
KorNLIKorSTSSNLIXNLI