论文标题
Kornli和Korsts：韩国自然语言理解的新基准数据集
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
论文作者
论文摘要
自然语言推论（NLI）和语义文本相似性（STS）是自然语言理解（NLU）的关键任务。尽管这些任务的几个基准数据集已使用英语和其他几种语言发布，但没有公开可用的NLI或STS数据集，韩语。在此的激励下，我们分别为韩国NLI和STS构建新数据集，分别为Kornli和Korsts。遵循以前的方法，我们将现有的英语培训集进行了机械翻译，并将开发和测试集转化为韩文。为了加快对韩国NLU的研究，我们还建立了Kornli和Korsts的基线。我们的数据集可在https://github.com/kakaobrain/kornludataset上公开获取。
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.
