KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
This provides a new benchmark for Korean NLU, addressing a language gap but is incremental as it adapts existing methods to a new language.
The authors tackled the lack of Korean datasets for natural language inference and semantic textual similarity by constructing and releasing KorNLI and KorSTS, achieving baseline results to accelerate research in Korean natural language understanding.
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.