Current Result Document :
| 한글제목(Korean Title) |
데이터 증강을 이용한 한국표준산업분류 다국어 분류 |
| 영문제목(English Title) |
Korean Standard Industry Classification Multilingual Classification Using Data Augmentation |
| 저자(Author) |
우찬균
오지은
황정윤
박재현
김지우
박진영
Chankyun Woo
Jieun Oh
Jeongyun Hwang
Jaehyeon Park
Jiwoo Kim
Jinyong Pak
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| 원문수록처(Citation) |
VOL 49 NO. 02 PP. 0696 ~ 0698 (2022. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we created a model that automatically classifies the foreign language survey industry classification items surveyed for foreigners in the Census conducted every five years by the Statistics Korea. A language model based on pre-training, which has been widely used recently, was used, and for multilingual classification, a classification model was constructed using XLM-R. Since the data to be used as learning data is in Korean, we first built a model in Korean and conducted a test in 6 languages1). Afterwards, the performance of the classification model according to the learning language was compared by machine translation of the learning data in Korean into English. As a result of comparison, the model that trained all 6 languages showed the best performance in the overall language with an average of 75%. |
| 키워드(Keyword) |
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| 파일첨부 |
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