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Findings of EMNLP 2023 - Accept

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공동 1저자로 참여한 “A Korean News Comments Dataset with Target-Specific Offensiveness Ratings” 논문이\nFindings of EMNLP 2023에 억셉되었습니다!

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해당 연구는 저희 튜닙과 숭실대 HUMANE Lab과 공동연구한 작업물입니다.

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제가 리서쳐보다는 엔지니어 성향이 강하고, 하는 일도 엔지니어 역할을 많이 하고 있어서 논문 writing과는 거리가 좀\n있는데, 21년도에 오픈소스 관련 논문 이후로 연구 관련으로는 첫 논문입니다 :)

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이 논문은 저희 Safety Check라는 서비스를 만들며 고민한 내용이 반영된 데이터셋 논문입니다.

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인터넷상에 존재하는 많은 혐오/차별적인 발언에 대해 저희가 정의한 11가지의 클래스와 각 클래스 별로 없음 (0) / 주의 (1) / 명백 (2) / 심함 (3) 과 같이 Likert 척도로 구분하여 라벨링했으며,\n해당 혐오의 대상에 대한 라벨링까지 되어 있는 데이터셋입니다.

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현재 공개된 많은 데이터셋들이 바이너리하게 1, 0으로 구분되어 혐오를 표현하는데, 문맥에 따라\n혐오 표현이 될 수도/안 될수도 있는 상황도 있고, 그 정도가 심한 경우와 그렇지 않은 경우에 대해서도 구분이 된다면\n실제 서비스 단에서 사용할 때 보다 유연하게 적용이 가능하다는 장점이 있습니다.

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이러한 저희의 고민에 대한 연구가 좋은 학회에 게재되게되어 해당 연구의 유종의 미를 거두게 된 것 같습니다.

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논문은 공개 가능한 시점에 바로 링크 달아 놓겠습니다!

","htmlAst":{"type":"root","children":[{"type":"element","tagName":"h1","properties":{},"children":[{"type":"text","value":"Findings of EMNLP 2023 - Accept"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"img","properties":{"src":"https://2023.emnlp.org/assets/images/logos/emnlp-2023-logo.png","height":150},"children":[]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"공동 1저자로 참여한 “A Korean News Comments Dataset with Target-Specific Offensiveness Ratings” 논문이\nFindings of EMNLP 2023에 억셉되었습니다!"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"해당 연구는 저희 튜닙과 숭실대 HUMANE Lab과 공동연구한 작업물입니다."}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"제가 리서쳐보다는 엔지니어 성향이 강하고, 하는 일도 엔지니어 역할을 많이 하고 있어서 논문 writing과는 거리가 좀\n있는데, 21년도에 오픈소스 관련 논문 이후로 연구 관련으로는 첫 논문입니다 :)"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"이 논문은 저희 Safety Check라는 서비스를 만들며 고민한 내용이 반영된 데이터셋 논문입니다."}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"인터넷상에 존재하는 많은 혐오/차별적인 발언에 대해 저희가 정의한 11가지의 클래스와 각 클래스 별로 "},{"type":"element","tagName":"code","properties":{"className":["language-text"]},"children":[{"type":"text","value":"없음 (0)"}]},{"type":"text","value":" / "},{"type":"element","tagName":"code","properties":{"className":["language-text"]},"children":[{"type":"text","value":"주의 (1)"}]},{"type":"text","value":" / "},{"type":"element","tagName":"code","properties":{"className":["language-text"]},"children":[{"type":"text","value":"명백 (2)"}]},{"type":"text","value":" / "},{"type":"element","tagName":"code","properties":{"className":["language-text"]},"children":[{"type":"text","value":"심함 (3)"}]},{"type":"text","value":" 과 같이 Likert 척도로 구분하여 라벨링했으며,\n해당 혐오의 대상에 대한 라벨링까지 되어 있는 데이터셋입니다."}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"현재 공개된 많은 데이터셋들이 바이너리하게 1, 0으로 구분되어 혐오를 표현하는데, 문맥에 따라\n혐오 표현이 될 수도/안 될수도 있는 상황도 있고, 그 정도가 심한 경우와 그렇지 않은 경우에 대해서도 구분이 된다면\n실제 서비스 단에서 사용할 때 보다 유연하게 적용이 가능하다는 장점이 있습니다."}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"이러한 저희의 고민에 대한 연구가 좋은 학회에 게재되게되어 해당 연구의 유종의 미를 거두게 된 것 같습니다."}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"논문은 공개 가능한 시점에 바로 링크 달아 놓겠습니다!"}]}],"data":{"quirksMode":false}},"excerpt":"Findings of EMNLP 2023 - Accept 공동 1저자로 참여한 “A Korean News Comments Dataset with Target-Specific Offensiveness Ratings” 논문이\nFindings of…","fields":{"readingTime":{"text":"3 min read"}},"frontmatter":{"title":"Findings of EMNLP 2023 Accept","userDate":"11 October 2023","date":"2023-10-11T12:00:00.000Z","tags":["nlp","record"],"excerpt":null,"image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/c3ae6255ff30abda220ecf1085e39f63/23fba/emnlp_2023.png","srcSet":"/static/c3ae6255ff30abda220ecf1085e39f63/37b55/emnlp_2023.png 750w,\n/static/c3ae6255ff30abda220ecf1085e39f63/f5738/emnlp_2023.png 1080w,\n/static/c3ae6255ff30abda220ecf1085e39f63/23fba/emnlp_2023.png 1134w","sizes":"100vw"},"sources":[{"srcSet":"/static/c3ae6255ff30abda220ecf1085e39f63/0b2ce/emnlp_2023.webp 750w,\n/static/c3ae6255ff30abda220ecf1085e39f63/74a67/emnlp_2023.webp 1080w,\n/static/c3ae6255ff30abda220ecf1085e39f63/f4bd7/emnlp_2023.webp 1134w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.517636684303351}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"children":[{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}]}}]}},"relatedPosts":{"totalCount":50,"edges":[{"node":{"id":"6529d72e-80e7-5d61-a672-d66276a3f641","excerpt":"MoE(Mixture of Experts) 기초부터 DeepSeek 혁신까지 작년 이맘때쯤 DeepSeek-V3가 저비용으로 엄청난 성능을 보이면서 화제가 되었습니다. 그 핵심 기술인 MoE(Mixture of Experts…","frontmatter":{"title":"MoE(Mixture of Experts) 기초부터 DeepSeek 혁신까지","date":"2026-01-15T01:11:55.000Z"},"fields":{"readingTime":{"text":"8 min read"},"slug":"/developed-moe/"}}},{"node":{"id":"8b49d3ef-4ce3-568c-9d71-240ff17fc3e0","excerpt":"BERT는 사실 Diffusion 모델이였다?! 최근 굉장히 흥미로운 글을 읽게되어 공유합니다. 원문 : link BERT와 Diffusion이 같은 방식이다?! 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2023.10.11","tags":["record"],"date":"2023-10-11T10:00:00.000Z","draft":false,"excerpt":null,"image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/113fb03190bacf5c5cc081cb5fe73513/90018/deadlift.png","srcSet":"/static/113fb03190bacf5c5cc081cb5fe73513/f503b/deadlift.png 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