효율적인 트랜스포머를 이용한 팩트체크 자동화 모델
Automated Fact Checking Model Using Efficient Transfomer
- 발행
- 2021 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 중앙대학교
- 출처
- 국내 KCI
- DOI
- 10.6109/jkiice.2021.25.9.1275
- 원문
- 원문 보기 ↗
개념
키워드
Automated fact checking, Locality sensitive hashing, Natural language processing, Transformer, Automated fact checking, Locality sensitive hashing, Natural language processing, Transformer
초록
Nowadays, fake news from newspapers and social media is a serious issue in news credibility. Some of machine learning methods (such as LSTM, logistic regression, and Transformer) has been applied for fact checking. In this paper, we present Transformer-based fact checking model which improves computational efficiency. Locality Sensitive Hashing (LSH) is employed to efficiently compute attention value so that it can reduce the computation time. With LSH, model can group semantically similar words, and compute attention value within the group. The performance of proposed model is 75% for accuracy, 42.9% and 75% for Fl micro score and F1 macro score, respectively.