Papers by Hyeonho Song
Detecting Offensive Language in an Open Chatbot Platform (2024.lrec-main)
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Hyeonho Song, Jisu Hong, Chani Jung, Hyojin Chin, Mingi Shin, Yubin Choi, Junghoi Choi, Meeyoung Cha
| Challenge: | Existing efforts to automatically filter offensive language are vulnerable to users’ deliberate text manipulation tactics, such as misspelling words. |
| Approach: | They propose a contrastive learning model that embeds chat content with a random masking strategy to detect offensive language in open-domain chat conversations. |
| Outcome: | The proposed model outperforms existing models in detecting offensive language in open-domain chat conversations while also showing robustness against users’ deliberate text manipulation tactics when using offensive language. |
Detecting Contextomized Quotes in News Headlines by Contrastive Learning (2023.findings-eacl)
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| Challenge: | Existing research has found a link between the use of direct quotations and fake news. |
| Approach: | They propose a contrastive learning framework that allows embedding news quotes based on domain-driven positive and negative samples to identify such an editorial strategy. |
| Outcome: | The proposed framework maximizes the semantic similarity between the headline quote and the matched quote in the body text while minimizing similarity for other unmatched quotes in the same or other articles. |