효율적인 트랜스포머를 이용한 팩트체크 자동화 모델Automated Fact Checking Model Using Efficient Transfomer
- Authors
- 윤희승; 정재은
- Issue Date
- Sep-2021
- Publisher
- 한국정보통신학회
- Keywords
- Automated fact checking; Locality sensitive hashing; Natural language processing; Transformer
- Citation
- 한국정보통신학회논문지, v.25, no.9, pp 1275 - 1278
- Pages
- 4
- Journal Title
- 한국정보통신학회논문지
- Volume
- 25
- Number
- 9
- Start Page
- 1275
- End Page
- 1278
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/50139
- DOI
- 10.6109/jkiice.2021.25.9.1275
- ISSN
- 2234-4772
2288-4165
- Abstract
- 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.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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