Recent Publications

Publications The latest 10 papers published or under review

Flexible acceptance condition of generics from a probabilistic viewpoint: Towards formalization of the semantics of generics.

Soo Hyun Ryu, Wonsuk Yang, and Jong C. Park
Journal of Psycholinguistic Research, 2022

Assessing automatic summarization model as a reading assistant

Aujin Kim, Jisu Shin, Soyeong Jeong, Sukmin Cho, and Jong C. Park
Proceedings of the Korea Computer Congress (KCC 2022), June 29-July 1, 2022

Constructing Korean Abusive Language Dataset using Machine Translation

Jisu Shin, Hoyun Song, Huije Lee, and Jong C. Park
Proceedings of the Korea Computer Congress (KCC 2022), June 29-July 1, 2022

Stopwords Mask Pooling for Dense Retrieval in Medical Domain

Dongho Choi, Hoyun Song, Soyeong Jeong, Sukmin Cho, and Jong C. Park
Proceedings of the Korea Computer Congress (KCC 2022), June 29-July 1, 2022

Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words

Jung-Ho Kim, Eui Jun Hwang, Sukmin Cho, Du Hui Lee, and Jong C. Park
Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022), June 21-23, 2022

GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages

Fitsum Gaim, Wonsuk Yang, and Jong C. Park
Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022), June 21-23, 2022

ELF22: A Context-based Counter-Trolling Dataset to Combat Internet Trolls

Huije Lee, Young Ju NA, Hoyun Song, Jisu Shin, and Jong C. Park
Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022), June 21-23, 2022
Show abstract
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 on our dataset shows a significantly improved performance in strategy-controlled sentence generation.

Template-based Document Labeling for Dense Retrieval

Sukmin Cho
MS Thesis, KAIST, 2022.

Data Augmentation for Abusive Language Detection via Back-translation and Domain Knowledge

Jisu Shin
MS Thesis, KAIST, 2022.

Query Generation with External Knowledge for Dense Retrieval

Sukmin Cho, Soyeong Jeong, Wonsuk Yang, and Jong C. Park
Proceedings of Deep Learning Inside Out (DeeLIO): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures