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

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.
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.
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
http://arxiv.org/abs/2004.03289v3