Huije Lee


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Generation of Korean Offensive Language by Leveraging Large Language Models via Prompt Design
Jisu Shin | Hoyun Song | Huije Lee | Fitsum Gaim | Jong Park
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires
Hoyun Song | Jisu Shin | Huije Lee | Jong Park
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Social media is one of the most highly sought resources for analyzing characteristics of the language by its users. In particular, many researchers utilized various linguistic features of mental health problems from social media. However, existing approaches to detecting mental disorders face critical challenges, such as the scarcity of high-quality data or the trade-off between addressing the complexity of models and presenting interpretable results grounded in expert domain knowledge. To address these challenges, we design a simple but flexible model that preserves domain-based interpretability. We propose a novel approach that captures the semantic meanings directly from the text and compares them to symptom-related descriptions. Experimental results demonstrate that our model outperforms relevant baselines on various mental disorder detection tasks. Our detailed analysis shows that the proposed model is effective at leveraging domain knowledge, transferable to other mental disorders, and providing interpretable detection results.


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ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls
Huije Lee | Young Ju Na | Hoyun Song | Jisu Shin | Jong Park
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for targeted individual users to handle the situation both quantitatively and qualitatively. To address this issue, we focus on automating the method to counter trolls, as counter responses to combat trolls encourage community users to maintain ongoing discussion without compromising freedom of expression. For this purpose, we propose a novel dataset for automatic counter response generation. In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy. We conducted three tasks to assess the effectiveness of our dataset and evaluated the results through both automatic and human evaluation. In human evaluation, we demonstrate that the model fine-tuned with our dataset shows a significantly improved performance in strategy-controlled sentence generation.


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A Large-scale Comprehensive Abusiveness Detection Dataset with Multifaceted Labels from Reddit
Hoyun Song | Soo Hyun Ryu | Huije Lee | Jong Park
Proceedings of the 25th Conference on Computational Natural Language Learning

As users in online communities suffer from severe side effects of abusive language, many researchers attempted to detect abusive texts from social media, presenting several datasets for such detection. However, none of them contain both comprehensive labels and contextual information, which are essential for thoroughly detecting all kinds of abusiveness from texts, since datasets with such fine-grained features demand a significant amount of annotations, leading to much increased complexity. In this paper, we propose a Comprehensive Abusiveness Detection Dataset (CADD), collected from the English Reddit posts, with multifaceted labels and contexts. Our dataset is annotated hierarchically for an efficient annotation through crowdsourcing on a large-scale. We also empirically explore the characteristics of our dataset and provide a detailed analysis for novel insights. The results of our experiments with strong pre-trained natural language understanding models on our dataset show that our dataset gives rise to meaningful performance, assuring its practicality for abusive language detection.

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Optimizing Domain Specificity of Transformer-based Language Models for Extractive Summarization of Financial News Articles in Korean
Huije Lee | Wonsuk Yang | Chaehun Park | Hoyun Song | Eugene Jang | Jong C. Park
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation