| { |
| "paper_id": "2020", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T14:43:56.244351Z" |
| }, |
| "title": "Open Korean Corpora: A Practical Report", |
| "authors": [ |
| { |
| "first": "Won", |
| "middle": [ |
| "Ik" |
| ], |
| "last": "Cho", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Seoul National University Seoul", |
| "location": { |
| "country": "Korea" |
| } |
| }, |
| "email": "wicho@hi.snu.ac.kr" |
| }, |
| { |
| "first": "Sangwhan", |
| "middle": [], |
| "last": "Moon", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "sangwhan@iki.fi" |
| }, |
| { |
| "first": "Youngsook", |
| "middle": [], |
| "last": "Song", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Kyung Hee University", |
| "location": { |
| "settlement": "Seoul", |
| "country": "Korea" |
| } |
| }, |
| "email": "youngsoksong@khu.ac.kr" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "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.", |
| "pdf_parse": { |
| "paper_id": "2020", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "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.", |
| "cite_spans": [], |
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| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "The Korean language is less explored in terms of corpus and computational linguistics, but its prevalence is often underrated. It regards about 80 million language users and is recently adopted in multilingual research as it is bound to CJK (Chinese, Japanese, and Korean), also handling a distinguished writing system.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "However, compared to the industrial need, the interest in Korean natural language processing (NLP) has not been developed much in international viewpoints, which recurrently hinders the related publication and further academic extension. Besides, in the recent NLP, where the benchmark practice is a trend, such systems lack at this point, deterring abroad and even native researchers who start Korean NLP from finding directions. Park et al. (2016) has shown a decent survey, but it seems that the techniques are mainly on the NLP pipeline. Also, albeit some curations on Korean NLP 1 and datasets 2 , we considered that little more organization is required, and better if internationally available. Our attempts are expected to mitigate the challenges that the researchers who handle Korean from a multi-or cross-lingual viewpoint may face.", |
| "cite_spans": [ |
| { |
| "start": 431, |
| "end": 449, |
| "text": "Park et al. (2016)", |
| "ref_id": "BIBREF27" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "In this paper, we scrutinize the struggles of government, institutes, industry, and individuals to construct public Korean NLP resources. First, we state how the institutional organizations have tackled the issue by making up the accessible resources, and point out the limitation thereof regarding international availability and license, to finally introduce and curate the fully public datasets along with the proposed criteria. Through this, we want to find out the current state of Korean corpora across the NLP tasks and whether they are freely or conditionally available. Our survey is to be curated and updated in the public repository 3 .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "With the increase in popularity of machine learningdriven methods in NLP, constructing a novel dataset and releasing it to the public can be considered the cornerstone of advancing research of a given language. While we believe many useful datasets exist behind industry walls, this is not particularly useful for advancing open research. Fortunately, there are organizations that construct and distribute cleaned, pre-processed datasets which are occasionally accompanied by a task and the annotation. In the context of Korean, there are numerous efforts in this field driven by governmentaffiliated organizations.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Accessible Resources", |
| "sec_num": "2" |
| }, |
| { |
| "text": "National Institute of Korean Language (NIKL) is an institution that establishes the norm for Korean linguistics 4 . However, at the same time, it usually undergoes the massive dataset construction from the view of computational linguistics, to apt to the new wave of language artificial intelligence (AI). Widely known ones include Korean word dictionaries 5 and Sejong Corpus (Kim, 2006) . The dictionary contains fundamental and new lexicons that make up Korean (along with the content), and the Sejong Corpus is a large-scale labeled NLP pipeline corpus for the tasks such as constituency and dependency parsing, mainly provided in .json format. Besides, recently, labeled corpora of about 300 million word size is released 6 , covering inter-sentence tasks such as similarity and entailment.", |
| "cite_spans": [ |
| { |
| "start": 377, |
| "end": 388, |
| "text": "(Kim, 2006)", |
| "ref_id": "BIBREF16" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Datasets from public institutions", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "Electronics and Telecommunications Research Institute (ETRI) has been collecting, refining, and tagging language processing and speech learning data over a long period of time 7 . Aside from NIKL, which mainly focuses on classical NLP pipelines, ETRI has also built a database for semantic analysis and question answering (QA), which are the outcome of a project Exo-brain 8 . The project includes syntax-semantic ones such as part of speech (POS) tagging and semantic role labeling (SRL), simultaneously providing construction guidelines for the corpora.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Datasets from public institutions", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "AI HUB is a platform organized by National Information Society Agency (NIA) in which a largescale dataset are integrated 9 . The datasets are built for various tasks at the government level, to promote the development of the AI industry. Provided resources are labeled or parallel corpora in reallife domains. Here, the domains are law, patent, common sense, open dialog, machine reading comprehension, and machine translation. Also, about 1,200 hours of speech corpus is provided to be used in spoken language modeling 10 . Recently, some new datasets have been distributed on wellness and emotional dialog, so that many people can have trials for social good and public AI. Also, open dictionary NIAdic 11 is freely available, provided by K-ICT Big Data Center. 5 The search portal is provided in https://stdict. korean.go.kr/main/main.do while the full word and content list are available at https://github.com/ korean-word-game/db 6 https://corpus.korean.go.kr/ 7 https://www.etri.re.kr/intro.html 8 http://exobrain.kr/pages/ko/result/ outputs.jsp 9 http://www.aihub.or.kr/ 10 https://www.aihub.or.kr/aidata/105 11 https://kbig.kr/portal/kbig/ knowledge/files/bigdata_report.page? bltnNo=10000000016451", |
| "cite_spans": [ |
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| "start": 764, |
| "end": 765, |
| "text": "5", |
| "ref_id": null |
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| "section": "Datasets from public institutions", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "The above datasets guarantee high quality, along with well-defined guidelines and the well-educated workers. However, their usage is often unfortunately confined to domestic researchers for procedural issues. Researchers abroad can indeed access the data, but they may face difficulty filling out and submitting the particular application form, instead of the barrier-free downloading system. Also, in most cases, modification and redistribution are restricted, making them uncompetitive in view of quality enhancement (Han et al., 2017) .", |
| "cite_spans": [ |
| { |
| "start": 519, |
| "end": 537, |
| "text": "(Han et al., 2017)", |
| "ref_id": "BIBREF15" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Accessibility", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "Here, we want to introduce datasets that can be utilized as an alternative to the limitedly accessible Korean NLP resources. Instead of scrutinizing all available corpora, we are going to curate them under specific criteria.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Accessibility", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "All the datasets to be introduced from now on are fully open access. This means that the dataset is downloadable with a single click or cloning, or at least one can acquire the dataset with simple signing. We set three checklists for the status of the corpus, namely documentation, usage, and redistribution. The first one is on how fine-grained the corpus description is.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Open Datasets", |
| "sec_num": "3" |
| }, |
| { |
| "text": "\u2022 Does the corpus have any documentation on the usage? (doc)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Open Datasets", |
| "sec_num": "3" |
| }, |
| { |
| "text": "\u2022 Does the corpus have a related article? 12 (art)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Open Datasets", |
| "sec_num": "3" |
| }, |
| { |
| "text": "\u2022 Does the corpus have a internationally available publication? (inter)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Open Datasets", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Next, we check whether the dataset is commercially available, academic use only, or unknown (com, acad, unk) . For the last one, We also investigate if redistribution is available with or without modification, if neither, or unknown (rd, rd/modx, no, unk). These attributes are noted along with each corpus title.", |
| "cite_spans": [ |
| { |
| "start": 92, |
| "end": 108, |
| "text": "(com, acad, unk)", |
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| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Open Datasets", |
| "sec_num": "3" |
| }, |
| { |
| "text": "KAIST Morpho-Syntactically Annotated Corpus [art, acad, no] applies morphological analysis to freely available KAIST raw corpus 13 . The scale is about 70M words and the domain includes novel, non-literature, article, etc.", |
| "cite_spans": [ |
| { |
| "start": 44, |
| "end": 59, |
| "text": "[art, acad, no]", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing and tagging", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "KAIST Korean Tree-Tagging Corpus [inter, acad, no] Choi et al. (1994) 14 bases on independently collected 30K sentences that are annotated according to the tree tagging scheme for Korean.", |
| "cite_spans": [ |
| { |
| "start": 33, |
| "end": 69, |
| "text": "[inter, acad, no] Choi et al. (1994)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing and tagging", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "UD Korean KAIST [inter, acad, no] Chun et al. 201815 applies universal dependency (UD) parsing (McDonald et al., 2013) to the Korean Tree-Tagging Corpus (Choi et al., 1994) .", |
| "cite_spans": [ |
| { |
| "start": 10, |
| "end": 33, |
| "text": "KAIST [inter, acad, no]", |
| "ref_id": null |
| }, |
| { |
| "start": 95, |
| "end": 118, |
| "text": "(McDonald et al., 2013)", |
| "ref_id": "BIBREF23" |
| }, |
| { |
| "start": 153, |
| "end": 172, |
| "text": "(Choi et al., 1994)", |
| "ref_id": "BIBREF6" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing and tagging", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "PKT-UD [inter, acad, no] Chun et al. 2018; Oh et al. 202016 applies UD parsing to the Penn Korean Treebank (Han et al., 2001) 17 .", |
| "cite_spans": [ |
| { |
| "start": 107, |
| "end": 125, |
| "text": "(Han et al., 2001)", |
| "ref_id": "BIBREF14" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing and tagging", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "KMOU NER [art, acad, rd] is a named entity recognition (NER) dataset built by Korean Marine and Ocean University 18 . The named entities are tagged for about 24K utterances according to name, time, and number. The data source are Exo-brain (by ETRI) and their own data combined, while the redistribution is available only for the latter.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing and tagging", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "AIR\u00d7NAVER NER/SRL [doc, acad, no] adopted the NER 19 and SRL 20 data constructed by Changwon National University for the purpose of a public competition 21 , and is annotated according to CoNLL format (Tjong Kim Sang and De Meulder, 2003) . Corpus size is about 90K and 35K each. and test set were constructed by human translation of XNLI (Conneau et al., 2018) . Just as in the original dataset, the pairs are labelled with entailment, contradiction, or neutral. About 940K examples are provided for training, and 2,490 and 5,010 respectively for dev and test. For KorSTS, the scoring was done from 0 to 5 to elaborate rather than the binary label that determines paraphrase. Following the scheme of NLI, 5,749 training data were machine translated using the STS-B dataset (Cer et al., 2017) as a source, while 1,500 dev set and 1,379 test set pairs are human translated.", |
| "cite_spans": [ |
| { |
| "start": 18, |
| "end": 33, |
| "text": "[doc, acad, no]", |
| "ref_id": null |
| }, |
| { |
| "start": 208, |
| "end": 238, |
| "text": "Kim Sang and De Meulder, 2003)", |
| "ref_id": "BIBREF30" |
| }, |
| { |
| "start": 339, |
| "end": 361, |
| "text": "(Conneau et al., 2018)", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 774, |
| "end": 792, |
| "text": "(Cer et al., 2017)", |
| "ref_id": "BIBREF1" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing and tagging", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "ParaKQC [inter, com, rd] Cho et al. (2020) 24originally consists of 10,000 questions and commands, and each instance is labeled with 4 topics (mail, smart agent, scheduling, and weather) and 4 speech acts (wh-question, alternative question, prohibition, and requirement). The sentence set can be extended to about 540K sentence pairs that determine sentence similarity and paraphrase.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Entailment and sentence similarity", |
| "sec_num": "3.2" |
| }, |
| { |
| "text": "NSMC [doc, com, rd] is a review sentiment corpus 25 of size 200K, which consists of Naver movie comments automatically labeled according to the methodology of Maas et al. (2011) . It adopts pos/neg binary labels, and it has been widely used as a benchmark for pretrained language models. BEEP! [inter, com, rd] Moon et al. (2020) is a hand-labeled, crowd-sourced dataset of about 9.4K Naver entertainment news comments with hate speech and social bias 26 . Bias and hate attribute consists of 3 labels, namely gender/others/none and hate/offensive/none, respectively. 3i4K [inter, com, rd] Cho et al. (2018) aims an utterance-level speech act classification of the Korean language 27 . The volume reaches 61K, handlabeled with 7 classes, namely fragment, statement, question, command, rhetorical question/command, and intonation-dependent utterances. KorQuAD 1.0, 2.0 [inter, com, rd/mod-x] provides human-generated QA corpus and leaderboard for Korean 28 . KorQuAD 1.0 (Lim et al., 2019) benchmarks SQuAD 1.0 (Rajpurkar et al., 2016) and consists of total 70K questions. KorQuAD 2.0 of size 100K aims at machine reading comprehension for structured HTML natural questions, which was created referring to the scheme of Google Natural Questions (Kwiatkowski et al., 2019) .", |
| "cite_spans": [ |
| { |
| "start": 159, |
| "end": 177, |
| "text": "Maas et al. (2011)", |
| "ref_id": "BIBREF22" |
| }, |
| { |
| "start": 970, |
| "end": 988, |
| "text": "(Lim et al., 2019)", |
| "ref_id": "BIBREF20" |
| }, |
| { |
| "start": 1010, |
| "end": 1034, |
| "text": "(Rajpurkar et al., 2016)", |
| "ref_id": "BIBREF29" |
| }, |
| { |
| "start": 1244, |
| "end": 1270, |
| "text": "(Kwiatkowski et al., 2019)", |
| "ref_id": "BIBREF17" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Sentence classification and QA", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "Sci-news-sum-kr [doc, acad, rd] contains about 50 Korean news summarizations generated by two Korean natives 29 . Since the size is not large, it is recommended to be used as a dev set. KAIST Translation Evaluation Set [doc, acad, no] is an evaluation set of size about 3,000 for enko MT 31 , augmented with index, original sentence, translation, related articles, and text source.", |
| "cite_spans": [ |
| { |
| "start": 16, |
| "end": 31, |
| "text": "[doc, acad, rd]", |
| "ref_id": null |
| }, |
| { |
| "start": 219, |
| "end": 234, |
| "text": "[doc, acad, no]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "KAIST Chinese-Korean Multilingual Corpus [doc, acad, no] contains 60K short sentence pairs for zh-ko MT 32 .", |
| "cite_spans": [ |
| { |
| "start": 41, |
| "end": 56, |
| "text": "[doc, acad, no]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "Transliteration Dataset [doc, com, rd] is not an official data repository 33 , but en-ko transliteration is collected from public dictionaries such as NIKL or Wiktionary 34 . A total of about 35K en (word) -ko (pronunciation) pairs are included.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "KAIST Transliteration Evaluation Set [doc, acad, no] is a word-pronunciation pair for phono-29 https://github.com/theeluwin/ sci-news-sum-kr-50", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "30 https://github.com/jungyeul/ korean-parallel-corpora 31 http://semanticweb.kaist.ac.kr/home/ index.php/Evaluateset2", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "32 http://semanticweb.kaist.ac.kr/home/ index.php/Corpus9", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "33 https://github.com/muik/ transliteration 34 https://en.wiktionary.org/wiki/ Wiktionary:Main_Page tactics in en-ko 35 , and consists of 7,186 words excerpted from the loanword dictionary 36 .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parallel corpora", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "Multilingual G2P Conversion [inter, com, rd] Gorman et al. (2020) is a shared task of SIGMOR-PHON 2020 37 , which aims to transform grapheme sequence into a phoneme sequence. The dataset was created with WikiPron 38 , and has been built for 10 languages including Korean (3,600 pairs for train, and 450 for dev/test each).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Korean in multilingual corpora", |
| "sec_num": "3.5" |
| }, |
| { |
| "text": "PAWS-X [inter, com, rd] Yang et al. 2019is a dataset that consists of 23,659 human translated PAWS evaluation pairs and about 300K machine-translated ones, for 6 languages including Korean 39 . Among them, Korean occupies about 5K train pairs, and 1,965 and 1,972 for dev/test each. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Korean in multilingual corpora", |
| "sec_num": "3.5" |
| }, |
| { |
| "text": "Speech datasets are usually massive, that a downloading via a single click is not necessarily guaranteed. Thus, we listed some of them as open even if they require some application form.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech corpora", |
| "sec_num": "3.6" |
| }, |
| { |
| "text": "KSS [doc, acad, rd] Park (2018) is a book corpus read by a female voice actress. 12K speech utterances and transcriptions are provided 42 .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech corpora", |
| "sec_num": "3.6" |
| }, |
| { |
| "text": "Zeroth [doc, com, rd] is an automatic speech recognition (ASR) dataset that contains approximately 50 hours of well-refined training data 43 . The speech corpus is provided free upon request and can be utilized for both research and commercial purposes.", |
| "cite_spans": [ |
| { |
| "start": 7, |
| "end": 21, |
| "text": "[doc, com, rd]", |
| "ref_id": null |
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| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech corpora", |
| "sec_num": "3.6" |
| }, |
| { |
| "text": "ClovaCall [inter, acad, no] Ha et al. 2020is an ASR dataset that consists of approximately 80 hours of telephone speech. The corpus is provided upon request, for only research purposes 44 .", |
| "cite_spans": [ |
| { |
| "start": 10, |
| "end": 27, |
| "text": "[inter, acad, no]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech corpora", |
| "sec_num": "3.6" |
| }, |
| { |
| "text": "Pansori-TED\u00d7KR [inter, acad, rd/mod-x] Choi and Lee 2018is an ASR dataset obtained by extracting the voices of Korean speakers from Pansori (Korean traditional song in colloquial style) and TED videos, with the transcription augmented 45 . The total reaches 3 hours, but it incorporates unique phonations that are not viable in other datasets.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech corpora", |
| "sec_num": "3.6" |
| }, |
| { |
| "text": "ProSem [inter, com, rd] Cho et al. (2019a) is a spoken language understanding corpus for syntactic ambiguity resolution in Korean, classifying spoken utterances into 7 speech acts 46 . For about 7,100 utterances recorded by two speakers, namely a male and a female, the ground truth text and label are annotated along with the English translation.", |
| "cite_spans": [ |
| { |
| "start": 7, |
| "end": 23, |
| "text": "[inter, com, rd]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech corpora", |
| "sec_num": "3.6" |
| }, |
| { |
| "text": "In total, we surveyed 32 corpora, namely 18 Korean text corpora, 9 multilingual corpora, and 5 speech corpora. They are composed of 7 datasets on parsing and tagging, 7 datasets on entailment, paraphrasing, and summarization, 8 datasets on (spoken language) classification, QA, and dialog, 5 datasets on machine translation/transliteraion, and 5 datasets on speech (pre-)processing 47 . We provide the full specification in Table 1 in the Appendix A. Documentation Ensuring that a curated list of resources is up-to-date is a challenge. In this regard, we aim to make our work open and canonical, as an online repository of curated resources for Korean. For the research community to have unconstrained access to all current open resources, while endorsing community contributions, the following criteria are crucial:", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 424, |
| "end": 431, |
| "text": "Table 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Summary", |
| "sec_num": "4" |
| }, |
| { |
| "text": "\u2022 The canonical, current version of this paper will be regularly published as a revision, e.g., on arxiv.org, based on a community-open version of this paper. \u2022 The resources will also have a corresponding registry, following the same metadata protocol for usability in different types of research, as we used in this protocol. \u2022 Each new contribution to the resource list will have a corresponding entry in the acknowledgments section.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Summary", |
| "sec_num": "4" |
| }, |
| { |
| "text": "We will make the registry machine parseable, so that other curated sites such as nlpprogress. org, can utilize the registry to automate updates. The project will be maintained as an open-source project, under a permissive license. A living document is a new territory for the field of academia, but we strongly believe that given the rapid progress of NLP research, this is an experiment worth attempting; and hope that a successful effort can inspire other languages to follow the same approach. Our approach is to be described in the public repository, guaranteeing the accessibility for domestic and abroad researchers. Also, a large portion of the data are expected to be more easily accessible via Koco 48 and Korpora 49 , the recently constructed dataset wrappers for Korean NLP.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Summary", |
| "sec_num": "4" |
| }, |
| { |
| "text": "In this paper, we investigated the Korean NLP datasets constructed and released as public resources. Our curation suggests a variety of open corpora that are freely available. This information will not only be helpful for the Korean researchers who want to start NLP, but also for the abroad ones who are interested in Korean NLP. Nonetheless, we think that Korean open corpora are still less disclosed or not yet sufficient. It is notable that the Korean government is currently supplying substantial funds to build a database. To guide this well, appropriate management and documentation should be guaranteed, so that the construction is meaningful and the outcome is internationally available.", |
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| "section": "Conclusion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "https://github.com/datanada/ Awesome-Korean-NLP 2 https://littlefoxdiary.tistory.com/42", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "https://github.com/ko-nlp/ Open-korean-corpora 4 https://www.korean.go.kr/", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Article is here more a complete form of document than doc above, and some domestic publications are included here since they are not internationally available.13 http://semanticweb.kaist.ac.kr/home/ index.php/KAIST_Corpus", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "https://github.com/warnikchow/paraKQC 25 https://github.com/e9t/nsmc 26 https://github.com/kocohub/ korean-hate-speech 27 https://github.com/warnikchow/3i4k 28 https://korquad.github.io/", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "https://github.com/inmoonlight/koco 49 https://github.com/ko-nlp/Korpora", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "The authors are grateful for all the contributors of the open Korean corpora. Special thanks goes to Seungyoung Lim, Jiyeon Ham, Jiyoon Han, Hyunjoong Kim and Jihyung Moon for checking and proofreading. We also appreciate team Ko-NLP for accommodating the public repository of our project.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgments", |
| "sec_num": null |
| }, |
| { |
| "text": "The labels in Docu. denote the level of description.\u2022 doc: If exists any document for the usage \u2022 art: If exists any complete form of article \u2022 inter: If exists a globally readable publication Other attributes regarding license has the following order of usage and redistribution availability: The specification on open Korean corpora. In Provider, Academia denotes universities and institutes, as well as the independent researchers who contribute to the community, while Industry means the companies or the research group thereof. Competition indicates the data used for the public competition, usually concerning both academia and industry. 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. We note Eval only if the dataset is not for the training purpose.", |
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| "eq_spans": [], |
| "section": "A Specification", |
| "sec_num": null |
| } |
| ], |
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| "FIGREF0": { |
| "text": "sae4K [inter, com, rd] Cho et al. (2019b) contains the directive sentence summarization of the sentence level. It includes about 50K pairs of utterance and natural language query pair for questions and commands, where the data is partly based on 3i4K (Cho et al., 2018) and some are humangenerate in concurrence with Cho et al. (2020). Korean Parallel Corpora [inter, acad, rd/modx] Park et al. (2016) contains about 100K en-ko sentence pairs for machine translation (MT). The data mainly bases on news articles, and now also provides the data on North Korean 30 .", |
| "num": null, |
| "type_str": "figure", |
| "uris": null |
| }, |
| "FIGREF1": { |
| "text": "TyDi-QA [inter, com, rd] Clark et al. (2020) pursues typological diversity in QA, and provides a total of 200,000 question-answer pairs for 11 linguistically diverse languages, including Korean 40 . Among them, Korean occupies about 11K train pairs, and 1,698/1,722 for dev/test each. XPersona [inter, com, rd] Lin et al. (2020) is a dataset for evaluating personalized chatbots 41 . It provides the dataset of Zhang et al. (2018) translated to 7 languages, including Korean, where Korean displays 299 dialogues with 4,684 utterances.", |
| "num": null, |
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| "uris": null |
| }, |
| "TABREF0": { |
| "num": null, |
| "html": null, |
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| "text": "Question Pair[doc, com, rd] consists of about 10,000 open domain sentence pairs 22 , with the binary labels that are hand-annotated on whether the sentences are paraphrase or irrelevant.KorNLI/KorSTS [inter, com, rd] Ham et al. (2020) is a natural language inference (NLI) and sentence textual similarity (STS) dataset for Korean 23 . For KorNLI, the train set was constructed by machine translating SNLI (Bowman et al., 2015) and MNLI, and the valid 14 http://semanticweb.kaist.ac.kr/home/ index.php/Corpus415 https://github.com/emorynlp/ud-korean 16 Also available at UD-Korean repository, but currently previous version. PKT v2020 data will be uploaded.17 https://catalog.ldc.upenn.edu/ LDC2006T09 LDC materials are not curated here.18 https://github.com/kmounlp/NER 19 http://air.changwon.ac.kr/?page_id=10 20 http://air.changwon.ac.kr/?page_id=14", |
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