Papers by Sunjae Kwon
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP (2024.findings-emnlp)
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Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu
| Challenge: | a new task is to generate lay definitions of medical terms in EHRs that are difficult to understand for patients. |
| Approach: | They propose a task of automatically generating lay definitions to simplify medical terms into patient-friendly lay language. |
| Outcome: | The proposed model can match or surpass state-of-the-art closed-source large language models like ChatGPT with high-quality data. |
MedJEx: A Medical Jargon Extraction Model with Wiki’s Hyperlink Span and Contextualized Masked Language Model Score (2022.emnlp-main)
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| Challenge: | Existing natural language processing (NLP) methods for identifying medical jargon terms are difficult for patients to understand. |
| Approach: | They propose a natural language processing application for identifying medical jargon terms from electronic health record notes. |
| Outcome: | The proposed model outperforms state-of-the-art models on an auxiliary Wikipedia hyperlink span dataset and on the annotated MedJ dataset. |
DischargeSim: A Simulation Benchmark for Educational Doctor–Patient Communication at Discharge (2025.emnlp-main)
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| Challenge: | Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. |
| Approach: | They propose a benchmark that evaluates large language models’ ability to act as personalized discharge educators. |
| Outcome: | Experiments with 18 LLMs show that model size does not always yield better education outcomes, highlighting trade-offs in strategy use and content prioritization. |
CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports (2023.findings-emnlp)
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Ye Chun, Sunjae Kwon, Kyunghwan Sohn, Nakwon Sung, Junyoup Lee, Byoung Seo, Kevin Compher, Seung-won Hwang, Jaesik Choi
| Challenge: | CR-COPEC is a large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. |
| Approach: | They propose a large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. |
| Outcome: | The proposed dataset can be used by investors and analysts without having to read through all the documents. |
ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection (2024.naacl-long)
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Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl, Minhee Sung, Joel Reisman, Wenjun Li, Robert Kerns, William Becker, Hong Yu
| Challenge: | Opioid related aberrant behaviors (ORABs) present novel risk factors for opioid overdose. |
| Approach: | They propose to use a biomedical natural language processing benchmark dataset to classify ORABs from patients’ EHR notes into nine categories: confirmed aberrant behavior, suggested aberrant behaviors, Opioids, indication, diagnosed opioid dependency, Benzodiazepines, medication changes, and Central Nervous System-related. |
| Outcome: | The proposed dataset outperforms two state-of-the-art models in most categories and the gains are especially higher among uncommon classes. |
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information (2023.acl-long)
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| Challenge: | Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. |
| Approach: | They propose an unsupervised approach that incorporates sense definitions when sense information of the answer is not provided. |
| Outcome: | The proposed approach improves the performance of the existing definition generation method in OOD examples. |
Chatbot To Help Patients Understand Their Health (2025.findings-emnlp)
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Won Seok Jang, Hieu Tran, Manav Shaileshkumar Mistry, Sai Kiran Gandluri, Yifan Zhang, Sharmin Sultana, Sunjae Kwon, Yuan Zhang, Zonghai Yao, Hong Yu
| Challenge: | NoteAid-Chatbot is a conversational AI designed to help patients better understand their health . |
| Approach: | They propose a new learning paradigm that leverages a multi-agent large language model and reinforcement learning framework without relying on costly human-generated training data. |
| Outcome: | The proposed framework surpasses non-expert human training methods. |
Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph (C18-1)
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| Challenge: | Existing methods to determine the word sense have fewer word set constraints than unsupervised methods. |
| Approach: | They propose a new word sense disambiguation method that generates the context of an ambiguous word by using similarities between an amorphous word and words in the input document. |
| Outcome: | The proposed method outperforms the baseline method on the SemEval-13 and SemEx15 datasets and has higher performance than the state-of-the-art unsupervised knowledge-based system. |