Papers by Chae-Gyun Lim
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)
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| Challenge: | Empathy is a multi-dimensional concept consisting of cognitive and affective aspects. |
| Approach: | They propose two new in-context example selection methods that utilize emotion and situational information. |
| Outcome: | The proposed method is effective in measuring the degree of human empathy. |
Korean TimeBank Including Relative Temporal Information (L18-1)
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| Challenge: | Temporal information extraction is one of the important research fields in natural language processing. |
| Approach: | They propose a concept of relative temporal information and supplement a Korean annotation language to represent new relative expressions and extend an annotated dataset through the revised language. |
| Outcome: | The proposed language can be used to represent relative temporal information and extend an annotated dataset, Korean TimeBank, through the revised language. |
PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling (2026.findings-acl)
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| Challenge: | Existing MI datasets do not explicitly model structured progression of MI phases, which is essential for effective and goal-oriented counseling. |
| Approach: | They propose a phase-structured MI dataset with a data generation framework that employs therapist, client, and supervisor LLMs to explicitly control phase transitions. |
| Outcome: | The proposed model achieves 12.3% better coverage of MI phases, 37.6% in guiding, and 61.1% in choosing. |
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision (2020.lrec-1)
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| Challenge: | Existing methods to annotate unlabeled data with emotions are expensive and time-consuming. |
| Approach: | They propose an annotation procedure that leverages Korean emotion lexicons and Korean-specific emotion features to annotate unlabeled data. |
| Outcome: | The proposed procedure compares with the KTEA dataset and a large-scale emotion-labeled dataset. |