KOAS: Korean Text Offensiveness Analysis System

San Hee Park, Kang Min Kim, Seonhee Cho, Jun Hyung Park, Hyuntae Park, Hyuna Kim, Seongwon Chung, Sang Keun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing
Subtitle of host publicationSystem Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages72-78
Number of pages7
ISBN (Electronic)9781955917117
Publication statusPublished - 2021
Event2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic
Duration: 2021 Nov 72021 Nov 11

Publication series

NameEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
Country/TerritoryDominican Republic
CityVirtual, Punta Cana
Period21/11/721/11/11

Bibliographical note

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.

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Information Systems

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