TY - GEN
T1 - KOAS
T2 - 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
AU - Park, San Hee
AU - Kim, Kang Min
AU - Cho, Seonhee
AU - Park, Jun Hyung
AU - Park, Hyuntae
AU - Kim, Hyuna
AU - Chung, Seongwon
AU - Lee, Sang Keun
N1 - Funding Information:
We thank the anonymous reviewers for their helpful comments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C3010430) and the Basic Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A4A1018309).
Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - Warning: This manuscript contains a certain level of offensive expression. As communication through social media platforms has grown immensely, the increasing prevalence of offensive language online has become a critical problem. Notably in Korea, one of the countries with the highest Internet usage, automatic detection of offensive expressions has recently been brought to attention. However, morphological richness and complex syntax of Korean causes difficulties in neural model training. Furthermore, most of previous studies mainly focus on the detection of abusive language, disregarding implicit offensiveness and underestimating a different degree of intensity. To tackle these problems, we present KOAS, a system that fully exploits both contextual and linguistic features and estimates an offensiveness score for a text. We carefully designed KOAS with a multi-task learning framework and constructed a Korean dataset for offensive analysis from various domains. Refer for a detailed demonstration.
AB - Warning: This manuscript contains a certain level of offensive expression. As communication through social media platforms has grown immensely, the increasing prevalence of offensive language online has become a critical problem. Notably in Korea, one of the countries with the highest Internet usage, automatic detection of offensive expressions has recently been brought to attention. However, morphological richness and complex syntax of Korean causes difficulties in neural model training. Furthermore, most of previous studies mainly focus on the detection of abusive language, disregarding implicit offensiveness and underestimating a different degree of intensity. To tackle these problems, we present KOAS, a system that fully exploits both contextual and linguistic features and estimates an offensiveness score for a text. We carefully designed KOAS with a multi-task learning framework and constructed a Korean dataset for offensive analysis from various domains. Refer for a detailed demonstration.
UR - http://www.scopus.com/inward/record.url?scp=85127233386&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85127233386
T3 - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
SP - 72
EP - 78
BT - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics (ACL)
Y2 - 7 November 2021 through 11 November 2021
ER -