Open Korean Corpora: A Living Document for Korean NLP Dataset Curation

Overview

• Korean, a language with 80M users is often overlooked in NLP research
• The availability of public datasets and tasks has hindered investigation
• Even the publicly available datasets are not always accompanied by English documentation and have poor discoverability
• Our work attempts to tackle this problem by curating a living document of open resources for the Korean language

PACLIC 2023

We will participate in PACLIC 37 and will present our paper "Revisiting Korean Corpus Studies through Technological Advances". If you visit PolyU@HK 3 December, please drop by!

NLP-OSS @ EMNLP 2020

We will be in the live session and monitoring the slide chat during EMNLP 2020. If you have any questions or would simply want to drop by to say hello, please drop by!

Public Institutions

Multiple government-funded institutions create datasets for the Korean language

• National Institute of Korean Language (NIKL)
• Electronics and Telecommunications Research Institute (ETRI)
• NIA AI HUB

Generally, government funded datasets tend to be very restrictive at allowing access to non-Korean citizens

Thus, Open Corpora here denotes a freely accessible and downloadable (at least only with a simple sign-in) dataset

Open Dataset for Korean NLP

Our work focuses on curating open Korean corpora under the following criteria:

• Documentation status
• License for use and distribution

Documentation and License

For documentation status Docu. the following holds.

• docu - Does the corpus have any documentation on the usage?
• paper - Does the corpus have a related article?
• int'l - Does the corpus have a internationally available publication?

License

For License, we check the followings:

• Commercially available (all), academic use only (academic), unknown (unk)
• Redistribution is available with/without modification (rd/no for rd and binary for mod-x), unknown (unk)

Other Attributes

• In Typical Usage, we note the area of intended use of the dataset.
• In Providers, we note if the dataset is provided by universities or institutes (Academia), companies or the research group thereof (Industry), or something combined, as Competition purpose.
• In Volume, (w) denotes words, (s) denotes sentences, (p) denotes pairs (either document or sentence pairs), (d) denotes dialogues, (h) denotes hours, and (u) denotes speech utterances.
• In Goal, Eval is noted if the purpose is suggested as an evaluation.
• In Lang, we note all the languages that the dataset handles.

View at a Glance

The table below describes the open Korean corpora investigated so far. To be updated along with our survey or PR. You can visit Here for the Korean description, and more information regarding government-driven database. We removed the hyperlink for pages not working.

1. Benchmark studies

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
BM-1	KLUE	Benchmark studies	Industry	int'l	all	rd	0	DP - 15K (s), DST - 10K (d), MRC - 29K (p), NER - 31K (s), NLI - 30K (p), RE - 48K (s), STS - 13K (p), TC - 64K (s)	Eval	ko	2021
BM-2	KoBEST	Benchmark studies	Industry	int'l	all	rd	0	BoolQ - 6K (p), COPA - 5K (s), KB-WiC - 6K (s), KB-HellaSwag - 3K (p & s), SentiNeg - 4K (s)	Eval	ko	2022

2. Parsing and tagging

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
PT-1	KAIST Morpho-Syntactically Annotated Corpus	Morphological analysis	Academia	paper	academic	no	0	70M (w)	-	ko	1999
PT-2	OpenKorPOS	Morphological analysis	Academia	int'l	all	rd	0	55M (w)	-	ko	2022
PT-3	KAIST Korean Tree-Tagging Corpus	Tree parsing	Academia	int'l	academic	no	0	30K (s)	-	ko	1994
PT-4	UD Korean KAIST	Dependency parsing	Academia	int'l	academic	no	1	27K (s)	-	ko	2018
PT-5	PKT-UD	Dependency parsing	Academia	int'l	academic	no	0	5K (s)	-	ko	2018
PT-6	KMOU NER	NER	Academia	paper	academic	rd	0	24K (s)	-	ko	2019
PT-7	AIR x NAVER NER	NER	Competition	docu	academic	no	0	90K (s)	-	ko	2018
PT-8	AIR x NAVER SRL	SRL	Competition	docu	academic	no	0	35K (s)	-	ko	2018
PT-9	KoNEC & KoNNEC	NER	Academia	docu	academic	no	0	26K(s)	-	ko	2022

3. Entailment, sentence similarity, and paraphrase

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
ESP-1	Question Pair	Paraphrase detection	Academia	docu	all	rd	0	10K (p)	-	ko	2018
ESP-2	KorNLI	NLI	Industry	int'l	all	rd	0	1,000K (p)	-	ko	2020
ESP-3	KorSTS	STS	Industry	int'l	all	rd	0	8,500K (p)	-	ko	2020
ESP-4	ParaKQC	Paraphrase detection	Academia	int'l	all	rd	0	540K (p)	-	ko	2020
ESP-5	StyleKQC	Paraphrase detection	Academia	int'l	all	rd	0	30K(s)	-	ko	2021
ESP-6	Korean Smile Style Dataset	Paraphrase detection	Industry	docu	academic	rd	0	2,5K(d)	-	ko	2022

4. Intention understanding, sentiment analysis, and offensive language detection

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
ISO-1	3i4K	Speech act classification	Academia	int'l	all	rd	0	55K / 6K (s)	-	ko	2019
ISO-2	NSMC	Sentiment analysis	Academia	docu	all	rd	0	150K / 50K (s)	-	ko	2015
ISO-3	Kocasm	Sentiment analysis	Academia	docu	all	rd	0	9K (s)	-	ko	2019
ISO-4	BEEP!	Hate speech detection	Academia	int'l	all	rd	0	8K / 0.5K / 1K (s)	-	ko	2020
ISO-5	APEACH	Hate speech detection	Academia	int'l	all	rd	0	4K	Eval	ko	2022
ISO-6	Unsmile	Hate speech detection	Industry	docu	academic	rd	0	19K	-	ko	2022
ISO-7	HateScore	Hate speech detection	Academia	int'l	academic	rd	1	35K	-	ko	2022
ISO-8	KOLD	Hate speech detection	Academia	int'l	all	rd	0	40K	-	ko	2022
ISO-9	K-MHaS	Hate speech detection	Academia	int'l	all	rd	0	109K	-	ko	2022
ISO-10	KODOLI	Hate speech detection	Academia	int'l	all	rd	0	38K	-	ko	2023
ISO-11	DKTC	Hate speech detection	Industry	docu	academic	rd	0	1.5K	-	ko	2022

5. QA and dialogue

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
QAD-1	KorQuAD 1.0	QA	Industry	int'l	all	rd	1	60K / 5K / 4K (p)	-	ko	2018
QAD-2	KorQuAD 2.0	QA	Industry	paper	all	rd	1	80K / 10K / 10K (p)	-	ko	2019
QAD-3	HuLiC	Dialog	Industry	docu	academic	rd	0	115K	-	ko	2022
QAD-4	OPELA	Dialog	Industry	int'l	academic	rd	0	560K (d)	-	ko	2022
QAD-5	CareCall	Dialog	Industry	int'l	academic	rd	0	10K	-	ko	2022

6. Summarization, Translation, and Transliteration

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
STT-1	Sci-news-sum-kr	Summarization	Academia	docu	academic	rd	0	50K (p)	Eval	ko	2016
STT-2	sae4K	Summarization	Academia	int'l	all	rd	0	50K (p)	-	ko	2020
STT-3	Korean Parallel Corpora	MT	Academia	int'l	all	rd	1	100K (p)	-	ko, en	2016
STT-4	KAIST Translation Evaluation Set	MT	Academia	docu	academic	no	0	3K (p)	Eval	ko, en	2000
STT-5	KAIST Chinese-Korean Multilingual Corpus	MT	Academia	docu	academic	no	0	60K (p)	-	ko, zh	2000
STT-6	Transliteration Dataset	Transliteration	Academia	docu	all	rd	0	35K (p)	-	ko, en	2016
STT-7	KAIST Transliteration Evaluation Set	Transliteration	Academia	docu	academic	no	0	7K (p)	Eval	ko, en	2000

7. Korean in multilingual corpora

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
KM-1	SIGMORPHON G2P	G2P conversion	Competition	int'l	all	rd	0	3.6K/ 0.45K / 0.45K (p)	-	ko, en, hy, bg, fr, ka, hi, hu, is, lt, el	2020
KM-2	PAWS-X	Paraphrase detection	Industry	int'l	all	rd	0	5K / 2K / 2K (p)	-	ko, fr, es, de, zh, ja	2019
KM-3	TyDi-QA	QA	Industry	int'l	all	rd	0	11K / 1,7K / 1,7K (p)	-	ko, en, ar, bn, fi, ja, id, sw, ru, te, th	2020
KM-4	XPersona	Dialog	Academia	int'l	all	rd	0	0.3K(d) / 4.7K (s)	-	ko, en, it, fr, id, zh, ja	2020
KM-5	MultiCoNER	NER	Competition	int'l	all	rd	0	178K / 2.6K (s)	-	ko, bn, de, en, es, fa, hi, nl, ru, tr, zh, multi, mix	2022
KM-6	Multilingual Tweet Intimacy Analysis	Sentiment analysis	Competition	int'l	unk	unk	0	2K (instances)	-	ko, en, fr, es, it, pt, nl, zh, hi, ar	2022
KM-7	IWSLT 2023	MT	Competition	int'l	all	rd	0	3K (p)	-	ko, en, vi, pt, ru	2023

8. Speech corpora

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
SL-1	KSS	ASR	Academia	docu	academic	rd	0	12+ (h) / 13K (u) / 1 speaker	-	ko	2018
SL-2	Zeroth	ASR	Industry	docu	all	rd	0	51+ (h) / 27K (s) / 46K (u) / 181 speakers	-	ko	2017
SL-3	ClovaCall	ASR	Industry	int'l	academic	no	0	80+ (h) / 60K (u) / 11K speakers	-	ko	2020
SL-4	Pansori-TedXKR	ASR	Academia	int'l	academic	rd	1	3+ (h) / 3K (u) / 41 speakers	-	ko	2018
SL-5	ProSem	SLU	Academia	int'l	all	rd	0	6+ (h) / 3,500 (s) / 7K (u) / 2 speakers	-	ko	2019
SL-6	kosp2e	Speech translation	Industry	int'l	academic	rd	0	39K(u)	-	ko, en	2021
SL-7	jejueo (JSS & JIT)	Other topics	Industry	int'l	all	rd	0	10K (JSS), 170K (JIT)	-	ko	2020

9. Other topics

No	Dataset	Typical Usage	Provider	Docu.	License	Redist.	mod-x	Volume	Goal	Lang.	Year
ETC-1	KoCHET	Other topics	Academia	int'l	academic	rd	0	NER 112K, RE 39K, ET 113K	-	ko	2022
ETC-2	KommonGen	Other topics	Academia	docu	all	rd	0	79K(s)	-	ko	2021
ETC-3	LBox Open	Other topics	Academia	int'l	academic	rd	0	150K	-	ko	2022
ETC-4	K2NLG	Other topics	Academia	int'l	academic	rd	0	4K(s)	-	ko	2020
ETC-5	Korean Ambiguity Data	Other topics	Academia	int'l	all	rd	0	35K	-	ko	2023
ETC-6	Korean GEC dataset	Other topics	Academia	int'l	academic	rd	0	155K(s pair)	-	ko	2022

Citation

To cite our work, please use the following: (Also available as cho-etal-2020-open in anthology.bib)

@inproceedings{cho-etal-2020-open,
    title = "Open {K}orean Corpora: A Practical Report",
    author = "Cho, Won Ik  and
      Moon, Sangwhan  and
      Song, Youngsook",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.12",
    pages = "85--93",
    abstract = "Korean is often referred to as a low-resource language in the research community. While this claim is partially true, it is also because the availability of resources is inadequately advertised and curated. This work curates and reviews a list of Korean corpora, first describing institution-level resource development, then further iterate through a list of current open datasets for different types of tasks. We then propose a direction on how open-source dataset construction and releases should be done for less-resourced languages to promote research.",
}

Contributing

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