KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
Jiyeon Ham | Yo Joong Choe | Kyubyong Park | Ilji Choi | Hyungjoon Soh
Findings of the Association for Computational Linguistics: EMNLP 2020
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.