Papers by Jean Lee
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection (2021.findings-acl)
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Henry Weld, Guanghao Huang, Jean Lee, Tongshu Zhang, Kunze Wang, Xinghong Guo, Siqu Long, Josiah Poon, Caren Han
| Challenge: | Existing toxic language detection models focus on the single utterance level without deeper understanding of context. |
| Approach: | They propose a dataset for in-game toxic language detection enabling joint intent classification and slot filling analysis, which is the core task of Natural Language Understanding (NLU). |
| Outcome: | The proposed framework handles utterance and token-level patterns, and rich contextual chatting history. |
3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding (2024.findings-acl)
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| Challenge: | Existing models for visually rich document understanding do not account for the diverse carriers of document versions and their associated noises. |
| Approach: | They propose a multimodal, multi-task, multiteacher joint-grained knowledge distillation model for visually-rich form document understanding. |
| Outcome: | The proposed model outperforms baselines on a comprehensive evaluation of public datasets showing it can handle complex structures and content of visually-rich forms. |
K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment (2022.coling-1)
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| Challenge: | Online hate speech detection resources in other languages are limited. |
| Approach: | They introduce a new dataset for hate speech detection that handles Korean language patterns. |
| Outcome: | The proposed dataset outperforms existing datasets in Korean language classifications. |
Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation (2025.emnlp-industry)
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| Challenge: | Recent advances in Large Language Models have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. |
| Approach: | They propose a method that rebalances the turn-count distribution of training data to mitigate Format Inertia in medical pre-consultation tasks. |
| Outcome: | The proposed method significantly alleviates Format Inertia in medical pre-consultation tasks. |
Taxonomy of Comprehensive Safety for Clinical Agents (2025.emnlp-industry)
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| Challenge: | Existing methods for ensuring safety in clinical chatbot applications are not suitable for clinical applications. |
| Approach: | They propose a fine-grained taxonomy that integrates safety filtering and tool selection into a single user intent classification step. |
| Outcome: | The proposed taxonomy integrates safety filtering and tool selection into a single user intent classification step. |
Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines (2026.eacl-industry)
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Jean Seo, Gibaeg Kim, Kihun Shin, Seungseop Lim, Hyunkyung Lee, Wooseok Han, Jongwon Lee, Eunho Yang
| Challenge: | EPAG is a benchmark dataset and evaluation pipeline for pre-consultation of large language models. |
| Approach: | They propose a benchmark dataset and framework for evaluating pre-consultation ability of LLMs using diagnostic guidelines. |
| Outcome: | The proposed framework outperforms frontier LLMs in pre-consultation. |
ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language (2024.lrec-main)
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| Challenge: | a new study examines the impact of natural language processing (NLP) on the endangered Manchu language. |
| Approach: | They propose to use BiLSTM-CRF, BERT, and mBERT to train transformer-based models on Manchu for NER and POS tagging tasks. |
| Outcome: | The proposed models achieved over 90% F1 score in both NER and POS tasks. |
Choosing Transfer Languages for Cross-Lingual Learning (P19-1)
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Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
| Challenge: | Cross-lingual transfer is a useful tool for improving performance of natural language processing (NLP) on low-resource languages. |
| Approach: | They propose to use cross-lingual transfer to improve accuracy of low-resource languages . they build models that consider features to perform prediction on such languages based on ranking problem . |
| Outcome: | The proposed model predicts good transfer languages much better than baselines considering single features in isolation. |