Younghun Lee


Towards Explaining Subjective Ground of Individuals on Social Media
Younghun Lee | Dan Goldwasser
Findings of the Association for Computational Linguistics: EMNLP 2022

Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual’s theory of mind and behavior from text is far from being resolved. This research proposes a neural model—Subjective Ground Attention—that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one’s previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual’s subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual’s subjective orientation towards abstract moral concepts.


Comparative Studies of Detecting Abusive Language on Twitter
Younghun Lee | Seunghyun Yoon | Kyomin Jung
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.