@inproceedings{ham-etal-2020-kornli,
title = "{K}or{NLI} and {K}or{STS}: New Benchmark Datasets for {K}orean Natural Language Understanding",
author = "Ham, Jiyeon and
Choe, Yo Joong and
Park, Kyubyong and
Choi, Ilji and
Soh, Hyungjoon",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.39",
doi = "10.18653/v1/2020.findings-emnlp.39",
pages = "422--430",
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 \url{https://github.com/kakaobrain/KorNLUDatasets}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
%A Ham, Jiyeon
%A Choe, Yo Joong
%A Park, Kyubyong
%A Choi, Ilji
%A Soh, Hyungjoon
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ham-etal-2020-kornli
%X 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.
%R 10.18653/v1/2020.findings-emnlp.39
%U https://aclanthology.org/2020.findings-emnlp.39
%U https://doi.org/10.18653/v1/2020.findings-emnlp.39
%P 422-430
Markdown (Informal)
[KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding](https://aclanthology.org/2020.findings-emnlp.39) (Ham et al., Findings 2020)
ACL