Papers by Jihyung Lee
MemeInterpret: Towards an All-in-One Dataset for Meme Understanding (2025.findings-emnlp)
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Jeongsik Park, Khoi P. N. Nguyen, Jihyung Park, Minseok Kim, Jaeheon Lee, Jae Won Choi, Kalyani Ganta, Phalgun Ashrit Kasu, Rohan Sarakinti, Sanjana Vipperla, Sai Sathanapalli, Nishan Vaghani, Vincent Ng
| Challenge: | Existing research has not explored meme captioning's decomposition into subtasks or its connections to other CMU tasks. |
| Approach: | a new meme corpus is built upon the Facebook Hateful Memes dataset . it contains meme captions, corresponding surface messages and relevant background knowledge . |
| Outcome: | a new corpus of meme captions and surface messages unifies three major categories of CMU tasks for the first time. |
KOLD: Korean Offensive Language Dataset (2022.emnlp-main)
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| Challenge: | Recent directions for offensive language detection focus on English and do not transfer well to other languages because of cultural and linguistic differences. |
| Approach: | They present a Korean offensive language dataset annotated with offensive language comments . they use the comments as training data for Korean BERT and RoBERTa models . |
| Outcome: | The proposed model improves offensiveness detection, target classification, and span detection while having room for improvement for target group classification and span prediction. |
Analyzing Norm Violations in Live-Stream Chat (2023.emnlp-main)
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Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
| Challenge: | Existing methods for detecting toxic language and norm violations are limited to live-streaming platforms . existing methods are less effective when applied to live streaming platforms based on a limited time frame . |
| Approach: | They propose to use contextual information to automatically moderate toxic content on live streaming platforms. |
| Outcome: | The proposed model improves on live-streaming platforms by 35%. |
EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL (2026.findings-acl)
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| Challenge: | Existing RL methods assign query-level rewards to all clauses, treating correct and incorrect clauses equally. |
| Approach: | They propose a method which provides fine-grained supervision through clause-level rewards. |
| Outcome: | Experiments on widely-used Text-to-SQL benchmarks show that EXPO-SqL outperforms existing methods by fine-grained clause-level learning. |
KoBLEX: Open Legal Question Answering with Multi-hop Reasoning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performances in general domains and are now extending into the expert domain of law. |
| Approach: | They propose a Korean Benchmark for Legal EXplainable QA (KoBLEX) that evaluates provision-grounded, multi-hop legal reasoning. |
| Outcome: | The proposed method outperforms baselines and shows a high correlation with human judgments. |
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph (2025.acl-long)
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| Challenge: | Existing methods for Text-to-SQL show little improvement compared to random selections . Existing approaches rely on intrinsic capabilities of hyper-scaled LLMs, not useful demonstrations. |
| Approach: | They propose a novel approach to effectively retrieving demonstrations and generating SQL queries by linking a question and its database schema items. |
| Outcome: | The proposed method shows consistent improvements in performance and efficiency across hyper-scaled LLMs and small LLM. |